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| library_name
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|---|---|---|---|---|---|---|---|---|---|
Kalray/regnet-x-1.6g | Kalray | "2024-06-25T06:39:37Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T06:39:37Z" | ---
license: mit
---
|
BenNyaMwa/Tickle | BenNyaMwa | "2024-06-25T06:44:37Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-25T06:40:31Z" | Entry not found |
Kalray/regnet-x-8.0g | Kalray | "2024-06-25T06:40:34Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T06:40:34Z" | ---
license: mit
---
|
Kalray/resnet101 | Kalray | "2024-06-25T06:40:48Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T06:40:48Z" | ---
license: mit
---
|
Kalray/resnet152 | Kalray | "2024-06-25T06:41:00Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T06:41:00Z" | ---
license: mit
---
|
Kalray/resnet18 | Kalray | "2024-06-25T06:41:16Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T06:41:16Z" | ---
license: mit
---
|
Kalray/resnet34 | Kalray | "2024-06-25T06:41:48Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T06:41:48Z" | ---
license: mit
---
|
kewu93/UltraMerge-v2-7B | kewu93 | "2024-06-25T06:45:26Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-25T06:42:00Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Kalray/resnet50 | Kalray | "2024-06-25T06:42:04Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T06:42:04Z" | ---
license: mit
---
|
Kalray/resnet50v2 | Kalray | "2024-06-25T06:42:20Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T06:42:20Z" | ---
license: mit
---
|
Kalray/resnext50 | Kalray | "2024-06-25T06:42:31Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T06:42:31Z" | ---
license: mit
---
|
Kalray/squeezenet | Kalray | "2024-06-25T06:42:52Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T06:42:52Z" | ---
license: mit
---
|
Kalray/vgg-16 | Kalray | "2024-06-25T06:43:05Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T06:43:05Z" | ---
license: mit
---
|
Kalray/vgg-19 | Kalray | "2024-06-25T06:43:19Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T06:43:19Z" | ---
license: mit
---
|
Kalray/xception | Kalray | "2024-06-25T06:43:32Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T06:43:32Z" | ---
license: mit
---
|
howdi2000/dell_v7 | howdi2000 | "2024-06-25T06:48:59Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-25T06:43:42Z" | ---
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** howdi2000
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
NemoSheng/llama2_fc_ft | NemoSheng | "2024-06-26T02:52:33Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-2-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-25T06:43:55Z" | ---
base_model: unsloth/llama-2-7b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** NemoSheng
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-2-7b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
prilipkod/AB | prilipkod | "2024-06-25T06:44:07Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-25T06:44:07Z" | Entry not found |
ILKT/2024-06-24_22-31-28_epoch_32 | ILKT | "2024-06-28T11:09:47Z" | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | "2024-06-25T06:44:48Z" | ---
language:
- en
- pl
model-index:
- name: 2024-06-24_22-31-28_epoch_32
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 25.497017892644138
- type: f1
value: 23.196505434372433
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 55.14
- type: ap
value: 15.181764997173907
- type: f1
value: 46.19755128974485
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 12.25018582615269
- type: v_measure_std
value: 1.9214164130465314
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 35.141223940820446
- type: f1
value: 32.44473134593861
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 35.51893753074275
- type: f1
value: 32.252471173888985
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 42.817753866846004
- type: f1
value: 40.824704534019986
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 42.66601082144614
- type: f1
value: 41.0074982856691
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 66.67535476397335
- type: ap
value: 75.51849268230839
- type: f1
value: 63.879935271884406
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 36.27372018128277
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 33.43365014862283
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 47.21606648199446
- type: f1
value: 48.37055884594939
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 24.412955465587046
- type: f1
value: 21.015586418343393
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
csk-2333/sd3m | csk-2333 | "2024-06-25T06:45:49Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-25T06:45:49Z" | ---
license: apache-2.0
---
|
Rosiness/GeoGPT4V-InternVL-Chat-40B | Rosiness | "2024-06-28T06:28:21Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"internvl_chat",
"feature-extraction",
"custom_code",
"region:us"
] | feature-extraction | "2024-06-25T06:45:56Z" | Entry not found |
Jaydip11/Jaydip_2004 | Jaydip11 | "2024-06-25T06:46:18Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-25T06:46:18Z" | ---
license: apache-2.0
---
|
PrunaAI/instruction-pretrain-InstructLM-500M-bnb-4bit-smashed | PrunaAI | "2024-06-25T06:46:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"pruna-ai",
"base_model:instruction-pretrain/InstructLM-500M",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-06-25T06:46:32Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: instruction-pretrain/InstructLM-500M
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with llm-int8.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo instruction-pretrain/InstructLM-500M installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/instruction-pretrain-InstructLM-500M-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/InstructLM-500M")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model instruction-pretrain/InstructLM-500M before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
yeye776/bert | yeye776 | "2024-06-25T08:08:05Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"electra",
"text-classification",
"generated_from_trainer",
"base_model:beomi/KcELECTRA-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-06-25T06:46:33Z" | ---
license: mit
base_model: beomi/KcELECTRA-base
tags:
- generated_from_trainer
model-index:
- name: bert
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert
This model is a fine-tuned version of [beomi/KcELECTRA-base](https://huggingface.co/beomi/KcELECTRA-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
PrunaAI/instruction-pretrain-InstructLM-500M-HQQ-1bit-smashed | PrunaAI | "2024-06-25T06:46:57Z" | 0 | 0 | transformers | [
"transformers",
"mistral",
"text-generation",
"pruna-ai",
"base_model:instruction-pretrain/InstructLM-500M",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-25T06:46:43Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: instruction-pretrain/InstructLM-500M
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo instruction-pretrain/InstructLM-500M installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/instruction-pretrain-InstructLM-500M-HQQ-1bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/instruction-pretrain-InstructLM-500M-HQQ-1bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/InstructLM-500M")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model instruction-pretrain/InstructLM-500M before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
RobertML/omnom | RobertML | "2024-06-25T06:57:54Z" | 0 | 0 | null | [
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | null | "2024-06-25T06:50:53Z" | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
castiel289/train_t5_model | castiel289 | "2024-06-25T06:54:18Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-25T06:54:18Z" | Entry not found |
BrundaL/wnli-finetuned-model | BrundaL | "2024-06-25T07:14:05Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"unsloth",
"generated_from_trainer",
"base_model:unsloth/mistral-7b-v0.3-bnb-4bit",
"license:apache-2.0",
"region:us"
] | null | "2024-06-25T06:54:20Z" | ---
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
library_name: peft
license: apache-2.0
tags:
- unsloth
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [unsloth/mistral-7b-v0.3-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-v0.3-bnb-4bit) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |
NitzanBar/sft_openassistant-guanaco | NitzanBar | "2024-06-25T06:54:41Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-25T06:54:41Z" | Entry not found |
jddllwqa/Qwen-Qwen1.5-7B-1719298562 | jddllwqa | "2024-06-25T06:56:14Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"region:us"
] | null | "2024-06-25T06:56:03Z" | ---
base_model: Qwen/Qwen1.5-7B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
jddllwqa/google-gemma-2b-1719298625 | jddllwqa | "2024-06-25T06:57:20Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | "2024-06-25T06:57:06Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
DonAgustinPasillo/bigbird_statement_validation_1 | DonAgustinPasillo | "2024-06-25T12:38:56Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-25T06:59:41Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
PrunaAI/AdaptLLM-finance-LLM-QUANTO-float8bit-smashed | PrunaAI | "2024-06-25T07:06:44Z" | 0 | 0 | null | [
"pruna-ai",
"base_model:AdaptLLM/finance-LLM",
"region:us"
] | null | "2024-06-25T07:00:30Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: AdaptLLM/finance-LLM
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo AdaptLLM/finance-LLM installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/AdaptLLM-finance-LLM-QUANTO-float8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-LLM")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model AdaptLLM/finance-LLM before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/AdaptLLM-finance-LLM-bnb-4bit-smashed | PrunaAI | "2024-06-25T07:02:29Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pruna-ai",
"base_model:AdaptLLM/finance-LLM",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-06-25T07:00:31Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: AdaptLLM/finance-LLM
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with llm-int8.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo AdaptLLM/finance-LLM installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/AdaptLLM-finance-LLM-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-LLM")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model AdaptLLM/finance-LLM before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/AdaptLLM-finance-LLM-QUANTO-int8bit-smashed | PrunaAI | "2024-06-25T07:07:28Z" | 0 | 0 | null | [
"pruna-ai",
"base_model:AdaptLLM/finance-LLM",
"region:us"
] | null | "2024-06-25T07:00:52Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: AdaptLLM/finance-LLM
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo AdaptLLM/finance-LLM installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/AdaptLLM-finance-LLM-QUANTO-int8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-LLM")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model AdaptLLM/finance-LLM before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/AdaptLLM-finance-LLM-QUANTO-int2bit-smashed | PrunaAI | "2024-06-25T07:07:22Z" | 0 | 0 | null | [
"pruna-ai",
"base_model:AdaptLLM/finance-LLM",
"region:us"
] | null | "2024-06-25T07:00:57Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: AdaptLLM/finance-LLM
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo AdaptLLM/finance-LLM installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/AdaptLLM-finance-LLM-QUANTO-int2bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-LLM")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model AdaptLLM/finance-LLM before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/AdaptLLM-finance-LLM-QUANTO-int4bit-smashed | PrunaAI | "2024-06-25T07:07:23Z" | 0 | 0 | null | [
"pruna-ai",
"base_model:AdaptLLM/finance-LLM",
"region:us"
] | null | "2024-06-25T07:00:59Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: AdaptLLM/finance-LLM
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo AdaptLLM/finance-LLM installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/AdaptLLM-finance-LLM-QUANTO-int4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-LLM")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model AdaptLLM/finance-LLM before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
nluai/phogpt-ft-v1 | nluai | "2024-06-25T07:01:22Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-25T07:01:22Z" | Entry not found |
PrunaAI/AdaptLLM-finance-LLM-HQQ-1bit-smashed | PrunaAI | "2024-06-25T07:02:26Z" | 0 | 0 | transformers | [
"transformers",
"llama",
"text-generation",
"pruna-ai",
"base_model:AdaptLLM/finance-LLM",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-25T07:01:41Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: AdaptLLM/finance-LLM
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo AdaptLLM/finance-LLM installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/AdaptLLM-finance-LLM-HQQ-1bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/AdaptLLM-finance-LLM-HQQ-1bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-LLM")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model AdaptLLM/finance-LLM before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
gokulsrinivasagan/test_dcn_clm | gokulsrinivasagan | "2024-06-25T07:02:17Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-25T07:02:17Z" | Entry not found |
PrunaAI/AdaptLLM-finance-LLM-HQQ-2bit-smashed | PrunaAI | "2024-06-25T07:03:32Z" | 0 | 0 | transformers | [
"transformers",
"llama",
"text-generation",
"pruna-ai",
"base_model:AdaptLLM/finance-LLM",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-25T07:02:22Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: AdaptLLM/finance-LLM
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo AdaptLLM/finance-LLM installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/AdaptLLM-finance-LLM-HQQ-2bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/AdaptLLM-finance-LLM-HQQ-2bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-LLM")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model AdaptLLM/finance-LLM before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/AdaptLLM-finance-LLM-HQQ-4bit-smashed | PrunaAI | "2024-06-25T07:04:27Z" | 0 | 0 | transformers | [
"transformers",
"llama",
"text-generation",
"pruna-ai",
"base_model:AdaptLLM/finance-LLM",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-25T07:02:27Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: AdaptLLM/finance-LLM
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo AdaptLLM/finance-LLM installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/AdaptLLM-finance-LLM-HQQ-4bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/AdaptLLM-finance-LLM-HQQ-4bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-LLM")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model AdaptLLM/finance-LLM before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
Multilingual-Multimodal-NLP/SEVENLLM-Qwen1.5-14B | Multilingual-Multimodal-NLP | "2024-06-25T07:46:30Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T07:02:53Z" | ---
license: mit
---
|
ILKT/2024-06-24_22-31-28_epoch_33 | ILKT | "2024-06-28T11:15:06Z" | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | "2024-06-25T07:04:17Z" | ---
language:
- en
- pl
model-index:
- name: 2024-06-24_22-31-28_epoch_33
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 25.815109343936378
- type: f1
value: 23.93430430463306
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 55.7
- type: ap
value: 15.141991115959222
- type: f1
value: 46.505333122966505
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 13.335941608704863
- type: v_measure_std
value: 2.4045296586367177
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 38.24815063887021
- type: f1
value: 35.12177827525203
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 38.11116576487949
- type: f1
value: 34.16663371347221
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 47.286482851378615
- type: f1
value: 45.39784348916099
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 47.058534185932125
- type: f1
value: 45.51386423044607
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 65.92817839559802
- type: ap
value: 75.16341100205705
- type: f1
value: 63.20011636917059
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 35.72678864835452
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 33.87094497300186
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 49.37673130193906
- type: f1
value: 50.701752579089785
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 19.0080971659919
- type: f1
value: 17.1562121390348
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
abitcoding/inset-gan | abitcoding | "2024-06-25T07:49:29Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-25T07:05:03Z" | |
tchen175/llama2-dolly-dataset-intel | tchen175 | "2024-06-25T07:05:44Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-25T07:05:44Z" | Entry not found |
jaykin01/Html | jaykin01 | "2024-06-25T07:08:01Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-25T07:08:00Z" | ---
license: apache-2.0
---
|
surya-narayanan/chemistry_non_masked | surya-narayanan | "2024-06-25T07:09:47Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-25T07:08:28Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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surya-narayanan/computer_science_non_masked | surya-narayanan | "2024-06-25T07:32:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-25T07:10:09Z" | ---
library_name: transformers
tags: []
---
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LarryAIDraw/Ines_pony | LarryAIDraw | "2024-06-25T07:18:47Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2024-06-25T07:10:46Z" | ---
license: creativeml-openrail-m
---
https://civitai.com/models/520327/inesarknights |
LarryAIDraw/utage_arknights__pony | LarryAIDraw | "2024-06-25T07:18:57Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2024-06-25T07:11:11Z" | ---
license: creativeml-openrail-m
---
https://civitai.com/models/528817/ponyutage-arknights |
AAA1988/RiskAct_V2 | AAA1988 | "2024-06-25T07:11:36Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-25T07:11:32Z" | ---
library_name: transformers
tags: []
---
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DBangshu/Base_gemma_e4_6_3 | DBangshu | "2024-06-25T09:37:22Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-25T07:12:23Z" | ---
library_name: transformers
tags: []
---
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Jacqueline9623/TestModelV0 | Jacqueline9623 | "2024-06-25T07:12:50Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-25T07:12:50Z" | ---
license: apache-2.0
---
|
KYAGABA/wav2vec2-large-xls-r-300m-rw-KinyarwandaTTSDataset-5hr-v2 | KYAGABA | "2024-06-25T10:24:18Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-25T07:12:53Z" | ---
library_name: transformers
tags: []
---
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kanghokh/att_model_2 | kanghokh | "2024-06-25T11:32:22Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-25T07:12:56Z" | ---
library_name: transformers
tags: []
---
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RaiRachit/distilbert-base-uncased-finetuned-IndonesianNER | RaiRachit | "2024-06-25T07:13:07Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-25T07:13:07Z" | Entry not found |
anushaporwal/w2v-bert-2.0-mongolian-colab-CV16.0 | anushaporwal | "2024-06-25T07:14:24Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-25T07:14:23Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Maks545curve/whisper-small-new-ru-pl-a | Maks545curve | "2024-06-27T02:50:28Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-25T07:15:42Z" | Entry not found |
jamalhface/Chatbot | jamalhface | "2024-06-25T07:16:15Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T07:16:15Z" | ---
license: mit
---
|
abdfajar707/llama3_8B_lora_model_rkp_v5 | abdfajar707 | "2024-06-25T07:47:05Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-25T07:16:23Z" | ---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** abdfajar707
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
DuongTrongChi/d-filter-v1.3 | DuongTrongChi | "2024-06-25T07:22:08Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:uitnlp/CafeBERT",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-06-25T07:20:01Z" | ---
license: apache-2.0
base_model: uitnlp/CafeBERT
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: d-filter-v1.3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# d-filter-v1.3
This model is a fine-tuned version of [uitnlp/CafeBERT](https://huggingface.co/uitnlp/CafeBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0751
- Accuracy: 0.9724
- F1: 0.8909
- Precision: 0.8985
- Recall: 0.8834
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0835 | 1.0 | 2828 | 0.0751 | 0.9724 | 0.8909 | 0.8985 | 0.8834 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.17.0
- Tokenizers 0.19.1
|
v0dkapapi/HospitalBOT | v0dkapapi | "2024-06-25T07:47:58Z" | 0 | 0 | null | [
"generated_from_trainer",
"base_model:ybelkada/falcon-7b-sharded-bf16",
"region:us"
] | null | "2024-06-25T07:20:17Z" | ---
base_model: ybelkada/falcon-7b-sharded-bf16
tags:
- generated_from_trainer
model-index:
- name: HospitalBOT
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HospitalBOT
This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.32.0
- Pytorch 2.3.0+cu121
- Datasets 2.13.1
- Tokenizers 0.13.3
|
oongaboongahacker/Gemini-Nano | oongaboongahacker | "2024-06-25T08:45:00Z" | 0 | 4 | null | [
"region:us"
] | null | "2024-06-25T07:22:51Z" | # GEMINI NANO EXTRACTED FROM CHROME CANARY VERSION 128.0.6557.0
If you have the particular version(>128), you can access the model here on windows:
C:\Users\USERNAME\AppData\Local\Google\Chrome SxS\User Data\OptGuideOnDeviceModel\[version]\weights.bin
You can signup for the built-in AI program here: https://developer.chrome.com/docs/ai/built-in
The model seems to be of the TFLite model format with the following architecture:
## Run the model using Mediapipe:
Instructions from here: https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/llm_inference/js
In a directory, make two files:
`index.html`:
```html
<!-- Copyright 2024 The MediaPipe Authors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. -->
<!doctype html>
<html lang="en">
<head>
<title>LLM Inference Web Demo</title>
</head>
<body>
Input:<br />
<textarea id="input" style="height: 300px; width: 600px"></textarea><br />
<input type="button" id="submit" value="Get Response" disabled /><br />
<br />
Result:<br />
<textarea id="output" style="height: 300px; width: 600px"></textarea>
<script type="module" src="index.js"></script>
</body>
</html>
```
`index.js`(Replace line no. 23 to the path to the .bin file):
```js
// Copyright 2024 The MediaPipe Authors.
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// ---------------------------------------------------------------------------------------- //
import {FilesetResolver, LlmInference} from 'https://cdn.jsdelivr.net/npm/@mediapipe/tasks-genai';
const input = document.getElementById('input');
const output = document.getElementById('output');
const submit = document.getElementById('submit');
const modelFileName = 'weights.bin'; /* PATH TO MODEL .bin */
/**
* Display newly generated partial results to the output text box.
*/
function displayPartialResults(partialResults, complete) {
output.textContent += partialResults;
if (complete) {
if (!output.textContent) {
output.textContent = 'Result is empty';
}
submit.disabled = false;
}
}
/**
* Main function to run LLM Inference.
*/
async function runDemo() {
const genaiFileset = await FilesetResolver.forGenAiTasks(
'https://cdn.jsdelivr.net/npm/@mediapipe/tasks-genai/wasm');
let llmInference;
submit.onclick = () => {
output.textContent = '';
submit.disabled = true;
llmInference.generateResponse(input.value, displayPartialResults);
};
submit.value = 'Loading the model...'
LlmInference
.createFromOptions(genaiFileset, {
baseOptions: {modelAssetPath: modelFileName},
// maxTokens: 512, // The maximum number of tokens (input tokens + output
// // tokens) the model handles.
// randomSeed: 1, // The random seed used during text generation.
// topK: 1, // The number of tokens the model considers at each step of
// // generation. Limits predictions to the top k most-probable
// // tokens. Setting randomSeed is required for this to make
// // effects.
// temperature:
// 1.0, // The amount of randomness introduced during generation.
// // Setting randomSeed is required for this to make effects.
})
.then(llm => {
llmInference = llm;
submit.disabled = false;
submit.value = 'Get Response'
})
.catch(() => {
alert('Failed to initialize the task.');
});
}
runDemo();
```
## Run using:
`python3 -m http.server 8000` in the same directory (or python -m SimpleHTTPServer 8000 for older python versions).
Finally,
`Open localhost:8000 in Chrome`
## License
I dont own shit. Just extracted the model weights stored as a .bin file. https://policies.google.com/terms/generative-ai/use-policy
|
Coolwowsocoolwow/Brian_Griffin | Coolwowsocoolwow | "2024-06-25T07:36:12Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-25T07:23:34Z" | ---
license: openrail
---
|
ILKT/2024-06-24_22-31-28_epoch_34 | ILKT | "2024-06-28T11:20:24Z" | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | "2024-06-25T07:23:47Z" | ---
language:
- en
- pl
model-index:
- name: 2024-06-24_22-31-28_epoch_34
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 25.685884691848905
- type: f1
value: 23.46336363481732
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 58.19999999999999
- type: ap
value: 16.145786474969245
- type: f1
value: 48.751898997617104
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 12.193476791185073
- type: v_measure_std
value: 2.48817916575776
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 30.65904505716207
- type: f1
value: 28.296338357638124
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 30.555828824397445
- type: f1
value: 27.34094423041823
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 41.079354404841965
- type: f1
value: 39.23454781067119
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 41.52975897688146
- type: f1
value: 39.93455414777503
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 70.22589052997394
- type: ap
value: 77.90537256270605
- type: f1
value: 67.73567932104206
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 36.373363736494106
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 33.76808076736949
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 49.16897506925208
- type: f1
value: 49.698649866936364
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 20.101214574898787
- type: f1
value: 18.184046628767042
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
|
sachithgunasekara/nanoJAXGPT | sachithgunasekara | "2024-06-25T07:33:23Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-25T07:25:07Z" | Private Repo(for the moment) used for pretraining:
https://bitbucket.org/paladinanalytics/nanojaxgpt |
Pranja/mistral-7b-unsloth-2 | Pranja | "2024-06-25T07:26:27Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-25T07:26:02Z" | ---
base_model: unsloth/mistral-7b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** Pranja
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Ariffiq99/KUCI_COPA_albert_base_finetuned | Ariffiq99 | "2024-06-25T10:02:25Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"albert",
"multiple-choice",
"generated_from_trainer",
"base_model:Ariffiq99/COPA_albert_base_finetuned",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | multiple-choice | "2024-06-25T07:27:04Z" | ---
license: apache-2.0
base_model: Ariffiq99/COPA_albert_base_finetuned
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: KUCI_COPA_albert_base_finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# KUCI_COPA_albert_base_finetuned
This model is a fine-tuned version of [Ariffiq99/COPA_albert_base_finetuned](https://huggingface.co/Ariffiq99/COPA_albert_base_finetuned) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3182
- F1: 0.3716
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.3403 | 1.0 | 5196 | 1.3278 | 0.3551 |
| 1.3295 | 2.0 | 10392 | 1.3223 | 0.3707 |
| 1.325 | 3.0 | 15588 | 1.3200 | 0.3668 |
| 1.3219 | 4.0 | 20784 | 1.3199 | 0.3654 |
| 1.3166 | 5.0 | 25980 | 1.3182 | 0.3716 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
munish0838/llama-3-8b-Instruct-Matter-0.1-Slim-A-lora | munish0838 | "2024-06-25T08:14:02Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-25T07:31:36Z" | ---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** munish0838
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
iitrsamrat/peft-starcoder-lora-a100 | iitrsamrat | "2024-06-25T07:32:02Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-25T07:32:02Z" | Entry not found |
surya-narayanan/economics_non_masked | surya-narayanan | "2024-06-25T08:06:45Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-25T07:32:51Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
manbeast3b/KinoInferTry16 | manbeast3b | "2024-06-25T07:35:44Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-25T07:33:48Z" | Entry not found |
mergekit-community/mergekit-ties-vrfhtfs | mergekit-community | "2024-06-25T07:40:43Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"arxiv:2306.01708",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:BioMistral/BioMistral-7B",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-25T07:35:30Z" | ---
base_model:
- mistralai/Mistral-7B-v0.1
- BioMistral/BioMistral-7B
- mistralai/Mistral-7B-Instruct-v0.2
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base.
### Models Merged
The following models were included in the merge:
* [BioMistral/BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B)
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
#no parameters necessary for base model
- model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
density: 0.5
weight: 0.5
- model: BioMistral/BioMistral-7B
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
normalize: false
int8_mask: true
dtype: float16
```
|
PrunaAI/instruction-pretrain-InstructLM-500M-HQQ-2bit-smashed | PrunaAI | "2024-06-25T07:38:15Z" | 0 | 0 | transformers | [
"transformers",
"mistral",
"text-generation",
"pruna-ai",
"base_model:instruction-pretrain/InstructLM-500M",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-25T07:37:58Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: instruction-pretrain/InstructLM-500M
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo instruction-pretrain/InstructLM-500M installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/instruction-pretrain-InstructLM-500M-HQQ-2bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/instruction-pretrain-InstructLM-500M-HQQ-2bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/InstructLM-500M")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model instruction-pretrain/InstructLM-500M before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
Helsngvash/Epicphotomakina | Helsngvash | "2024-06-25T17:21:04Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-25T07:39:19Z" | Entry not found |
PrunaAI/instruction-pretrain-InstructLM-500M-QUANTO-int2bit-smashed | PrunaAI | "2024-06-25T07:40:15Z" | 0 | 0 | null | [
"pruna-ai",
"base_model:instruction-pretrain/InstructLM-500M",
"region:us"
] | null | "2024-06-25T07:39:40Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: instruction-pretrain/InstructLM-500M
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo instruction-pretrain/InstructLM-500M installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/instruction-pretrain-InstructLM-500M-QUANTO-int2bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/InstructLM-500M")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model instruction-pretrain/InstructLM-500M before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/instruction-pretrain-InstructLM-500M-HQQ-4bit-smashed | PrunaAI | "2024-06-25T07:40:01Z" | 0 | 0 | transformers | [
"transformers",
"mistral",
"text-generation",
"pruna-ai",
"base_model:instruction-pretrain/InstructLM-500M",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-25T07:39:42Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: instruction-pretrain/InstructLM-500M
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo instruction-pretrain/InstructLM-500M installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/instruction-pretrain-InstructLM-500M-HQQ-4bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/instruction-pretrain-InstructLM-500M-HQQ-4bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/InstructLM-500M")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model instruction-pretrain/InstructLM-500M before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
Reihaneh/wav2vec2_fy_nl_en_de_lid_common_voice_47 | Reihaneh | "2024-06-25T07:41:37Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-25T07:41:36Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
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## Model Card Contact
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jddllwqa/Qwen-Qwen1.5-0.5B-1719301297 | jddllwqa | "2024-06-25T07:41:38Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-25T07:41:38Z" | Entry not found |
PrunaAI/instruction-pretrain-InstructLM-500M-QUANTO-int8bit-smashed | PrunaAI | "2024-06-25T07:42:30Z" | 0 | 0 | null | [
"pruna-ai",
"base_model:instruction-pretrain/InstructLM-500M",
"region:us"
] | null | "2024-06-25T07:41:52Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: instruction-pretrain/InstructLM-500M
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo instruction-pretrain/InstructLM-500M installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/instruction-pretrain-InstructLM-500M-QUANTO-int8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/InstructLM-500M")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model instruction-pretrain/InstructLM-500M before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/instruction-pretrain-InstructLM-500M-QUANTO-int4bit-smashed | PrunaAI | "2024-06-25T07:42:29Z" | 0 | 0 | null | [
"pruna-ai",
"base_model:instruction-pretrain/InstructLM-500M",
"region:us"
] | null | "2024-06-25T07:41:52Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: instruction-pretrain/InstructLM-500M
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo instruction-pretrain/InstructLM-500M installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/instruction-pretrain-InstructLM-500M-QUANTO-int4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/InstructLM-500M")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model instruction-pretrain/InstructLM-500M before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
PrunaAI/instruction-pretrain-InstructLM-500M-QUANTO-float8bit-smashed | PrunaAI | "2024-06-25T07:43:39Z" | 0 | 0 | null | [
"pruna-ai",
"base_model:instruction-pretrain/InstructLM-500M",
"region:us"
] | null | "2024-06-25T07:43:01Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: instruction-pretrain/InstructLM-500M
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with quanto.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo instruction-pretrain/InstructLM-500M installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/instruction-pretrain-InstructLM-500M-QUANTO-float8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/InstructLM-500M")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model instruction-pretrain/InstructLM-500M before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
jayashreedevi2020/wav2vec2-large-xls-r-300m-assamese_speech_to_IPA_with_mer_cer_f1_accuracy | jayashreedevi2020 | "2024-06-25T07:43:41Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-25T07:43:39Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
woweenie/v72-curated2-5e6-bs6ga12-3k-main-22k-10xmulresume-24k-cd0-pok-cvn-hd-11k-half | woweenie | "2024-06-25T07:47:18Z" | 0 | 0 | diffusers | [
"diffusers",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-06-25T07:44:24Z" | Entry not found |
srbdtwentyfour/paligemma_captioning_ep1 | srbdtwentyfour | "2024-06-26T08:18:21Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-25T07:45:55Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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Yaaaxa/Yaazapro | Yaaaxa | "2024-06-25T07:47:34Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-25T07:47:34Z" | ---
license: apache-2.0
---
|
abdfajar707/llama3_8B_lora_model_rkp_v1.1 | abdfajar707 | "2024-06-28T09:43:32Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-25T07:47:53Z" | ---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** abdfajar707
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
dingsonye/demo1 | dingsonye | "2024-06-25T07:48:04Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-25T07:48:04Z" | ---
license: apache-2.0
---
|
kohankhaki/Llama-3-8B_SST5-Grouped_IDX-3 | kohankhaki | "2024-06-25T07:57:17Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-classification | "2024-06-25T07:48:30Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Glossary [optional]
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TaeukHwang/Test | TaeukHwang | "2024-06-25T07:48:59Z" | 0 | 0 | null | [
"license:llama3",
"region:us"
] | null | "2024-06-25T07:48:59Z" | ---
license: llama3
---
|
mogmyij/Llama2-7b-BoolQ-layers-20-31-2epoch | mogmyij | "2024-06-25T07:49:38Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | "2024-06-25T07:49:31Z" | ---
license: llama2
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-hf
model-index:
- name: Llama2-7b-BoolQ-layers-20-31-2epoch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama2-7b-BoolQ-layers-20-31-2epoch
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5654
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5165 | 0.9996 | 1178 | 0.5795 |
| 0.2894 | 1.9992 | 2356 | 0.5654 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1 |
lingvenvist/all_xlwsd_mid | lingvenvist | "2024-06-25T07:52:02Z" | 0 | 0 | transformers | [
"transformers",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2024-06-25T07:50:33Z" | Entry not found |
bezzam/digicam-celeba-mmcn-unet4M | bezzam | "2024-06-25T07:51:00Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T07:50:33Z" | ---
license: mit
---
|
johntsi/ZeroSwot-Medium_asr-cv_mt-covost2_en-to-15 | johntsi | "2024-06-25T12:12:01Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"zero_swot_encoder",
"feature-extraction",
"zeroswot",
"speech translation",
"zero-shot",
"end-to-end",
"nllb",
"wav2vec2",
"automatic-speech-recognition",
"custom_code",
"en",
"ar",
"ca",
"de",
"et",
"fa",
"id",
"ja",
"lv",
"mn",
"sl",
"sv",
"ta",
"tr",
"zh",
"dataset:mozilla-foundation/common_voice_8_0",
"arxiv:2402.10422",
"license:mit",
"region:us"
] | automatic-speech-recognition | "2024-06-25T07:51:09Z" | ---
language:
- en
- ar
- ca
- de
- et
- fa
- id
- ja
- lv
- mn
- sl
- sv
- ta
- tr
- zh
license: mit
metrics:
- bleu
datasets:
- mozilla-foundation/common_voice_8_0
pipeline_tag: automatic-speech-recognition
tags:
- zeroswot
- speech translation
- zero-shot
- end-to-end
- nllb
- wav2vec2
---
# ZeroSwot ✨🤖✨
<!-- <div style='display:flex; gap: 0.25rem; '>
<a href='https://arxiv.org/abs/2402.10422'><img src='https://img.shields.io/badge/paper-PDF-green'></a>
<a href='https://github.com/mt-upc/ZeroSwot/blob/main/LICENSE'><img src='https://img.shields.io/badge/License-MIT-blue.svg'></a>
<a href='https://github.com/mt-upc/ZeroSwot'><img src='https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white'></a>
</div> -->
ZeroSwot is a state-of-the-art zero-shot end-to-end Speech Translation system.
<div align=center><img src="resources/intro.png" height="65%" width="65%"/></div>
The model is created by adapting a wav2vec2.0-based encoder to the embedding space of NLLB, using a novel subword compression module and Optimal Transport, while only utilizing ASR data. It thus enables **Zero-shot E2E Speech Translation to all the 200 languages supported by NLLB**.
For more details please refer to our [paper](https://arxiv.org/abs/2402.10422) and the [original repo](https://github.com/mt-upc/ZeroSwot) build on fairseq.
## Architecture
The compression module is a light-weight transformer that takes as input the hidden state of wav2vec2.0 and the corresponding CTC predictions, and compresses them to subword-like embeddings similar to those expected from NLLB and aligns them using Optimal Transport. For inference we simply pass the output of the speech encoder to NLLB encoder.
<div align=center><img src="resources/methodology.png" height="120%" width="120%"/></div>
## Version
This version of ZeroSwot is trained with ASR data from CommonVoice. It adapts [wav2vec2.0-large](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) to the embedding space of the [nllb-200-distilled-600M_covost2](https://huggingface.co/johntsi/nllb-200-distilled-600M_covost2_en-to-15) model, which is a multilingually finetuned NLLB on MuST-C MT data.
We have more versions available:
| Models | ASR data | NLLB version |
|:------:|:--------:|:------------:|
| [ZeroSwot-Medium_asr-mustc](https://huggingface.co/johntsi/ZeroSwot-Medium_asr-mustc_en-to-200) | MuST-C v1.0 | [distilled-600M original](https://huggingface.co/facebook/nllb-200-distilled-600M)|
| [ZeroSwot-Medium_asr-mustc_mt-mustc](https://huggingface.co/johntsi/ZeroSwot-Medium_asr-mustc_mt-mustc_en-to-8) | MuST-C v1.0 | [distilled-600M finetuned w/ MuST-C](https://huggingface.co/johntsi/nllb-200-distilled-600M_mustc_en-to-8) |
| [ZeroSwot-Large_asr-mustc](https://huggingface.co/johntsi/ZeroSwot-Large_asr-mustc_en-to-200) | MuST-C v1.0 | [distilled-1.3B original](https://huggingface.co/facebook/nllb-200-distilled-1.3B) |
| [ZeroSwot-Large_asr-mustc_mt-mustc](https://huggingface.co/johntsi/ZeroSwot-Large_asr-mustc_mt-mustc_en-to-8) | MuST-C v1.0 | [distilled-1.3B finetuned w/ MuST-C](https://huggingface.co/johntsi/nllb-200-distilled-1.3B_mustc_en-to-8) |
| [ZeroSwot-Medium_asr-cv](https://huggingface.co/johntsi/ZeroSwot-Medium_asr-cv_en-to-200) | CommonVoice | [distilled-600M original](https://huggingface.co/facebook/nllb-200-distilled-600M)|
| [ZeroSwot-Medium_asr-cv_mt-covost2](https://huggingface.co/johntsi/ZeroSwot-Medium_asr-cv_mt-covost2_en-to-15) | CommonVoice | [distilled-600M finetuned w/ CoVoST2](https://huggingface.co/johntsi/nllb-200-distilled-600M_covost2_en-to-15) |
| [ZeroSwot-Large_asr-cv](https://huggingface.co/johntsi/ZeroSwot-Large_asr-cv_en-to-200) | CommonVoice | [distilled-1.3B original](https://huggingface.co/facebook/nllb-200-distilled-1.3B) |
| [ZeroSwot-Large_asr-cv_mt-covost2](https://huggingface.co/johntsi/ZeroSwot-Large_asr-cv_mt-covost2_en-to-15) | CommonVoice | [distilled-1.3B finetuned w/ CoVoST2](https://huggingface.co/johntsi/nllb-200-distilled-1.3B_covost2_en-to-15) |
## Usage
The model is tested with python 3.9.16 and Transformer v4.41.2. Install also torchaudio and sentencepiece for processing.
```bash
pip install transformers torchaudio sentencepiece
```
```python
from transformers import Wav2Vec2Processor, NllbTokenizer, AutoModel, AutoModelForSeq2SeqLM
import torchaudio
def load_and_resample_audio(audio_path, target_sr=16000):
audio, orig_freq = torchaudio.load(audio_path)
if orig_freq != target_sr:
audio = torchaudio.functional.resample(audio, orig_freq=orig_freq, new_freq=target_sr)
audio = audio.squeeze(0).numpy()
return audio
# Load processors and tokenizers
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
tokenizer = NllbTokenizer.from_pretrained("johntsi/nllb-200-distilled-600M_covost2_en-to-15")
# Load ZeroSwot Encoder
commit_hash = "4cebecf19220ee375078046392290fcc12d1e321"
zeroswot_encoder = AutoModel.from_pretrained(
"johntsi/ZeroSwot-Medium_asr-cv_mt-covost2_en-to-15", trust_remote_code=True, revision=commit_hash,
)
zeroswot_encoder.eval()
zeroswot_encoder.to("cuda")
# Load NLLB Model
nllb_model = AutoModelForSeq2SeqLM.from_pretrained("johntsi/nllb-200-distilled-600M_covost2_en-to-15")
nllb_model.eval()
nllb_model.to("cuda")
# Load audio file
audio = load_and_resample_audio(path_to_audio_file) # you can use "resources/sample.wav" for testing
input_values = processor(audio, sampling_rate=16000, return_tensors="pt").to("cuda")
# translation to German
compressed_embeds, attention_mask = zeroswot_encoder(**input_values)
predicted_ids = nllb_model.generate(
inputs_embeds=compressed_embeds,
attention_mask=attention_mask,
forced_bos_token_id=tokenizer.lang_code_to_id["deu_Latn"],
num_beams=5,
)
translation = tokenizer.decode(predicted_ids[0], skip_special_tokens=True)
print(translation)
```
## Results
BLEU scores on CoVoST-2 test compared to supervised SOTA models XLS-R-1B and SeamlessM4T-Medium. You can refer to Table 5 of the Results section in the paper for more details.
| Models | ZS | Size (B) | Ar | Ca | Cy | De | Et | Fa | Id | Ja | Lv | Mn | Sl | Sv | Ta | Tr | Zh | Average |
|:--------------:|:----:|:----------:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:-------:|
| [XLS-R-1B](https://huggingface.co/facebook/wav2vec2-xls-r-1b) | ✗ | 1.0 | 19.2 | 32.1 | **31.8** | 26.2 | 22.4 | 21.3 | 30.3 | 39.9 | 22.0 | 14.9 | 25.4 | 32.3 | 18.1 | 17.1 | 36.7 | 26.0 |
| [SeamlessM4T-Medium](https://huggingface.co/facebook/seamless-m4t-medium) | ✗ | 1.2 | 20.8 | 37.3 | 29.9 | **31.4** | 23.3 | 17.2 | 34.8 | 37.5 | 19.5 | 12.9 | 29.0 | 37.3 | 18.9 | **19.8** | 30.0 | 26.6 |
| [ZeroSwot-M_asr-cv](https://huggingface.co/johntsi/ZeroSwot-Medium_asr-cv_en-to-200) | ✓ | 0.35/0.95 | 17.6 | 32.5 | 18.0 | 29.9 | 20.4 | 16.3 | 32.4 | 32.0 | 13.3 | 10.0 | 25.2 | 34.4 | 17.8 | 15.6 | 30.5 | 23.1 |
| [ZeroSwot-M_asr-cv_mt-covost2](https://huggingface.co/johntsi/ZeroSwot-Medium_asr-cv_mt-covost2_en-to-200) | ✓ | 0.35/0.95 | **24.4** | **38.7** | 28.8 | 31.2 | **26.2** | **26.0** | **36.0** | **46.0** | **24.8** | **19.0** | **31.6** | **37.8** | **24.4** | 18.6 | **39.0** | **30.2** |
## Citation
If you find ZeroSwot useful for your research, please cite our paper :)
```
@misc{tsiamas2024pushing,
title={{Pushing the Limits of Zero-shot End-to-End Speech Translation}},
author={Ioannis Tsiamas and Gerard I. Gállego and José A. R. Fonollosa and Marta R. Costa-jussà},
year={2024},
eprint={2402.10422},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
moon4656/Llama-3-Open-Ko-8B-Instruct-moon4656 | moon4656 | "2024-06-25T07:55:53Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:beomi/Llama-3-Open-Ko-8B-Instruct-preview",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-25T07:51:39Z" | ---
base_model: beomi/Llama-3-Open-Ko-8B-Instruct-preview
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** moon4656
- **License:** apache-2.0
- **Finetuned from model :** beomi/Llama-3-Open-Ko-8B-Instruct-preview
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
bezzam/diffusercam-mirflickr-unet4M-unrolled-admm20-unet4M | bezzam | "2024-06-25T07:52:14Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-25T07:51:50Z" | ---
license: mit
---
|
starnet/07-star21-06-25 | starnet | "2024-06-25T07:58:52Z" | 0 | 0 | null | [
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | null | "2024-06-25T07:52:06Z" | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
asr-africa/wav2vec2-large-xls-r-300m-kallama-19hr-v1 | asr-africa | "2024-06-26T15:00:03Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-25T07:52:32Z" | Entry not found |
ILKT/2024-06-24_22-31-18_epoch_36 | ILKT | "2024-06-28T15:47:50Z" | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"ILKT",
"sentence-similarity",
"mteb",
"feature-extraction",
"custom_code",
"en",
"pl",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | "2024-06-25T07:52:34Z" | ---
language:
- en
- pl
model-index:
- name: 2024-06-24_22-31-18_epoch_36
results:
- dataset:
config: default
name: MTEB AllegroReviews
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 22.007952286282304
- type: f1
value: 20.184749199881114
task:
type: Classification
- dataset:
config: default
name: MTEB CBD
revision: 36ddb419bcffe6a5374c3891957912892916f28d
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 55.44
- type: ap
value: 15.155807502579838
- type: f1
value: 46.042670544423075
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
split: test
type: PL-MTEB/cdsce-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
split: test
type: PL-MTEB/cdscr-sts
metrics: []
task:
type: STS
- dataset:
config: default
name: MTEB EightTagsClustering
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 10.137982623459788
- type: v_measure_std
value: 1.4257058129364055
task:
type: Clustering
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 31.644250168123744
- type: f1
value: 28.821039825441037
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 31.087063453025088
- type: f1
value: 27.820419594804513
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 39.5191661062542
- type: f1
value: 37.59440975149745
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 38.35710772257747
- type: f1
value: 36.72533005697207
task:
type: Classification
- dataset:
config: default
name: MTEB PAC
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 66.15696495800753
- type: ap
value: 74.79168753410772
- type: f1
value: 63.010224743378615
task:
type: Classification
- dataset:
config: default
name: MTEB PSC
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
split: test
type: PL-MTEB/psc-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB PlscClusteringP2P
revision: 8436dd4c05222778013d6642ee2f3fa1722bca9b
split: test
type: PL-MTEB/plsc-clustering-p2p
metrics:
- type: v_measure
value: 35.49041870956889
task:
type: Clustering
- dataset:
config: default
name: MTEB PlscClusteringS2S
revision: 39bcadbac6b1eddad7c1a0a176119ce58060289a
split: test
type: PL-MTEB/plsc-clustering-s2s
metrics:
- type: v_measure
value: 31.68952276148132
task:
type: Clustering
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: d90724373c70959f17d2331ad51fb60c71176b03
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 48.53185595567867
- type: f1
value: 49.53073186056529
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 21.558704453441297
- type: f1
value: 18.540694263260875
task:
type: Classification
- dataset:
config: default
name: MTEB SICK-E-PL
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics: []
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R-PL
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
split: test
type: PL-MTEB/sickr-pl-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: dev
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
- dataset:
config: pl
name: MTEB STSBenchmarkMultilingualSTS (pl)
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
split: test
type: mteb/stsb_multi_mt
metrics: []
task:
type: STS
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- mteb
- feature-extraction
---
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