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|---|---|---|---|---|---|---|---|---|---|
XLS/Reformer-BC | XLS | "2024-06-12T13:57:39Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T13:49:51Z" | ---
license: apache-2.0
---
See below:
https://github.com/xilinshen/Reformer/ |
Rychiy/Meta-Llama-3-8B-Instruct-Lohnabrechnung | Rychiy | "2024-06-12T13:52:03Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T13:52:03Z" | Entry not found |
sshadrunov/Tatu | sshadrunov | "2024-06-12T13:52:22Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T13:52:22Z" | ---
license: apache-2.0
---
|
Alexxxo/alexxxo | Alexxxo | "2024-06-12T13:54:02Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T13:54:02Z" | Entry not found |
Blackdalalrian/dalarian | Blackdalalrian | "2024-06-12T13:55:06Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2024-06-12T13:55:06Z" | ---
license: other
license_name: dalarian
license_link: LICENSE
---
|
T0m0hir0/girl | T0m0hir0 | "2024-06-12T13:55:33Z" | 0 | 0 | null | [
"license:unknown",
"region:us"
] | null | "2024-06-12T13:55:33Z" | ---
license: unknown
---
|
N16069/finetuned-indian-food | N16069 | "2024-06-12T13:57:18Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T13:57:18Z" | Entry not found |
xjyplayer/tuiwen_ckpt | xjyplayer | "2024-06-12T13:59:41Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T13:59:41Z" | ---
license: apache-2.0
---
|
zopo9300/546457 | zopo9300 | "2024-06-12T13:59:42Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T13:59:42Z" | Entry not found |
Augusto777/vit-base-patch16-224-ve-U14-b-24 | Augusto777 | "2024-06-12T14:14:37Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-12T13:59:55Z" | ---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-ve-U14-b-24
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8478260869565217
---
<!-- 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. -->
# vit-base-patch16-224-ve-U14-b-24
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6698
- Accuracy: 0.8478
## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 24
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.97 | 7 | 1.3673 | 0.4783 |
| 1.3789 | 1.93 | 14 | 1.2760 | 0.5435 |
| 1.2878 | 2.9 | 21 | 1.1732 | 0.5435 |
| 1.2878 | 4.0 | 29 | 1.0471 | 0.5435 |
| 1.128 | 4.97 | 36 | 0.9960 | 0.5435 |
| 0.9873 | 5.93 | 43 | 0.8939 | 0.6304 |
| 0.8611 | 6.9 | 50 | 0.8650 | 0.6087 |
| 0.8611 | 8.0 | 58 | 0.8442 | 0.6304 |
| 0.7397 | 8.97 | 65 | 0.7331 | 0.7174 |
| 0.6326 | 9.93 | 72 | 0.6698 | 0.8478 |
| 0.6326 | 10.9 | 79 | 0.7430 | 0.7391 |
| 0.5424 | 12.0 | 87 | 0.7030 | 0.7609 |
| 0.4687 | 12.97 | 94 | 0.6352 | 0.8043 |
| 0.404 | 13.93 | 101 | 0.5498 | 0.8043 |
| 0.404 | 14.9 | 108 | 0.5631 | 0.8043 |
| 0.3244 | 16.0 | 116 | 0.5706 | 0.8261 |
| 0.305 | 16.97 | 123 | 0.6010 | 0.8043 |
| 0.2819 | 17.93 | 130 | 0.5845 | 0.7826 |
| 0.2819 | 18.9 | 137 | 0.5594 | 0.8043 |
| 0.2487 | 20.0 | 145 | 0.5567 | 0.8043 |
| 0.2297 | 20.97 | 152 | 0.5489 | 0.8043 |
| 0.2297 | 21.93 | 159 | 0.5556 | 0.7826 |
| 0.2177 | 22.9 | 166 | 0.5519 | 0.8043 |
| 0.2177 | 23.17 | 168 | 0.5515 | 0.8043 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Rey-23/Baek-hyun | Rey-23 | "2024-06-12T14:24:07Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-12T14:00:12Z" | ---
license: openrail
---
|
aiintelligentsystems/nextlevelbert-128 | aiintelligentsystems | "2024-06-12T14:02:27Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T14:02:17Z" | Entry not found |
DBangshu/GPT2_0_5 | DBangshu | "2024-06-12T14:02:48Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-12T14:02:26Z" | ---
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] |
Smit1/zombie | Smit1 | "2024-06-12T14:02:41Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T14:02:41Z" | ---
license: apache-2.0
---
|
MG31/Detr_new_1 | MG31 | "2024-06-12T14:02:42Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:02:42Z" | Entry not found |
HyperdustProtocol/HyperAoto-llama2-7b-812 | HyperdustProtocol | "2024-06-12T14:02:55Z" | 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 | "2024-06-12T14:02:43Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-2-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** HyperdustProtocol
- **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)
|
mengshyu/llava-1.5-7b-hf-q4f16_1-MLC | mengshyu | "2024-06-12T14:28:13Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:04:08Z" | Entry not found |
beatrixlayla22/add_more_detail_v1 | beatrixlayla22 | "2024-06-12T14:04:44Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:04:22Z" | Entry not found |
feedback-to-code/swe-diff-llama-3-8B-Instruct-Test | feedback-to-code | "2024-06-12T14:04:59Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:04:59Z" | Entry not found |
domvsca/lora_model_dome | domvsca | "2024-06-12T14:06:03Z" | 0 | 1 | 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 | "2024-06-12T14:05:34Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** domvsca
- **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)
|
silviocol/nasar | silviocol | "2024-06-12T14:16:56Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-12T14:05:37Z" | ---
license: openrail
---
|
SuckerPunch1488/Picture | SuckerPunch1488 | "2024-06-12T14:06:10Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:06:10Z" | Entry not found |
dominic1021/sd3 | dominic1021 | "2024-06-12T14:09:06Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:06:43Z" | Entry not found |
Sjskk/Iww | Sjskk | "2024-06-12T14:06:45Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T14:06:45Z" | ---
license: apache-2.0
---
|
szajean/CharliXCX | szajean | "2024-06-12T14:08:23Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-12T14:07:49Z" | ---
license: openrail
---
|
ebretbnyh/Federal-appeals-court-upholds-California-law-banning-gun-shows-at-county-fairs-ab-updated | ebretbnyh | "2024-06-12T14:08:11Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:08:11Z" | Entry not found |
Yartret/test | Yartret | "2024-06-12T14:10:01Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T14:10:01Z" | ---
license: apache-2.0
---
|
Ilya1422/SergeyMavrodi-RVC-500Epoch-RMVPE-40k | Ilya1422 | "2024-06-12T15:46:41Z" | 0 | 1 | null | [
"region:us"
] | null | "2024-06-12T14:11:17Z" | # This is the RVC model of the famous Russian leader of the joint-stock company "MMM" in the 1990s.
## Note: To maximize the similarity, you should set the TUNE parameter to about 4-5 and be sure to set the method to rmvpe. Just play around with the settings in Voice Changer AI.
|
btynhethb/Federal-appeals-court-upholds-California-law-banning-gun-shows-at-county-fairs-41-updated | btynhethb | "2024-06-12T14:12:29Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:12:29Z" | Entry not found |
evelyn0414/OPERA | evelyn0414 | "2024-06-12T14:20:31Z" | 0 | 0 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | null | "2024-06-12T14:12:56Z" | ---
license: cc-by-nc-4.0
---
<div align="center">
<a href="https://github.com/evelyn0414/OPERA"> <img width="200px" height="200px" src="https://github.com/evelyn0414/OPERA/assets/61721952/6d17e3e7-5b3f-4e0b-991a-1cc02c5434dc"></a>
</div>
OPERA is an OPEn Respiratory Acoustic foundation model pretraining and benchmarking system. We curate large-scale respiratory audio datasets (136K samples, 440 hours), pretrain three pioneering foundation models, and build a benchmark consisting of 19 downstream respiratory health tasks for evaluation. Our pretrained models demonstrate superior performance (against existing acoustic models pretrained with general audio on 16 out of 19 tasks) and generalizability (to unseen datasets and new respiratory audio modalities). This highlights the great promise of respiratory acoustic foundation models and encourages more studies using OPERA as an open resource to accelerate research on respiratory audio for health.
## Usage
The code is available at: https://github.com/evelyn0414/OPERA
## Citation
Kindly cite our work if you find it useful.
|
aigc11/tuiwen_sd_models | aigc11 | "2024-06-13T23:35:05Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:13:13Z" | Entry not found |
aiintelligentsystems/nextlevelbert-256 | aiintelligentsystems | "2024-06-12T14:14:18Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T14:14:08Z" | Entry not found |
Stanislav91/sffw | Stanislav91 | "2024-06-12T14:16:03Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:15:00Z" | зимний пейзаж в россии |
Augusto777/vit-base-patch16-224-ve-U15-b-80 | Augusto777 | "2024-06-12T14:38:02Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-12T14:16:24Z" | ---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-ve-U15-b-80
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8695652173913043
---
<!-- 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. -->
# vit-base-patch16-224-ve-U15-b-80
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5357
- Accuracy: 0.8696
## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 80
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.97 | 7 | 1.3872 | 0.1304 |
| 1.3844 | 1.93 | 14 | 1.3826 | 0.1739 |
| 1.3552 | 2.9 | 21 | 1.3500 | 0.2391 |
| 1.3552 | 4.0 | 29 | 1.2528 | 0.2174 |
| 1.2458 | 4.97 | 36 | 1.1474 | 0.2391 |
| 1.0668 | 5.93 | 43 | 1.1376 | 0.3913 |
| 0.9335 | 6.9 | 50 | 1.0063 | 0.4348 |
| 0.9335 | 8.0 | 58 | 0.9238 | 0.5870 |
| 0.8059 | 8.97 | 65 | 0.8241 | 0.8043 |
| 0.6774 | 9.93 | 72 | 0.7625 | 0.7826 |
| 0.6774 | 10.9 | 79 | 0.7096 | 0.8043 |
| 0.5346 | 12.0 | 87 | 0.6368 | 0.8261 |
| 0.4427 | 12.97 | 94 | 0.5741 | 0.8261 |
| 0.3557 | 13.93 | 101 | 0.5441 | 0.8261 |
| 0.3557 | 14.9 | 108 | 0.5258 | 0.8478 |
| 0.2637 | 16.0 | 116 | 0.5430 | 0.8261 |
| 0.2356 | 16.97 | 123 | 0.5773 | 0.8261 |
| 0.1844 | 17.93 | 130 | 0.7222 | 0.7391 |
| 0.1844 | 18.9 | 137 | 0.6537 | 0.7826 |
| 0.1765 | 20.0 | 145 | 0.5458 | 0.8043 |
| 0.1362 | 20.97 | 152 | 0.5777 | 0.8478 |
| 0.1362 | 21.93 | 159 | 0.6256 | 0.7826 |
| 0.1467 | 22.9 | 166 | 0.7330 | 0.7826 |
| 0.1614 | 24.0 | 174 | 0.7743 | 0.7609 |
| 0.1246 | 24.97 | 181 | 0.5763 | 0.8261 |
| 0.1246 | 25.93 | 188 | 0.5994 | 0.8261 |
| 0.1058 | 26.9 | 195 | 0.6926 | 0.8043 |
| 0.0943 | 28.0 | 203 | 0.6406 | 0.8478 |
| 0.1 | 28.97 | 210 | 0.6940 | 0.7609 |
| 0.1 | 29.93 | 217 | 0.6193 | 0.8261 |
| 0.0865 | 30.9 | 224 | 0.5357 | 0.8696 |
| 0.0852 | 32.0 | 232 | 0.8015 | 0.7826 |
| 0.0852 | 32.97 | 239 | 0.6680 | 0.8478 |
| 0.0721 | 33.93 | 246 | 0.8469 | 0.7826 |
| 0.0749 | 34.9 | 253 | 0.6682 | 0.8261 |
| 0.0876 | 36.0 | 261 | 0.7474 | 0.8261 |
| 0.0876 | 36.97 | 268 | 0.6501 | 0.8696 |
| 0.0677 | 37.93 | 275 | 0.6918 | 0.8043 |
| 0.0574 | 38.9 | 282 | 0.7001 | 0.8478 |
| 0.0573 | 40.0 | 290 | 0.7119 | 0.8261 |
| 0.0573 | 40.97 | 297 | 0.8317 | 0.8043 |
| 0.0663 | 41.93 | 304 | 0.7456 | 0.8043 |
| 0.0685 | 42.9 | 311 | 0.7242 | 0.8261 |
| 0.0685 | 44.0 | 319 | 0.6971 | 0.8043 |
| 0.0431 | 44.97 | 326 | 0.7439 | 0.8261 |
| 0.0529 | 45.93 | 333 | 0.8210 | 0.8043 |
| 0.0698 | 46.9 | 340 | 0.7114 | 0.8043 |
| 0.0698 | 48.0 | 348 | 0.6985 | 0.8478 |
| 0.054 | 48.97 | 355 | 0.8860 | 0.8261 |
| 0.0528 | 49.93 | 362 | 0.8942 | 0.8043 |
| 0.0528 | 50.9 | 369 | 0.9411 | 0.8043 |
| 0.0478 | 52.0 | 377 | 0.8705 | 0.7826 |
| 0.041 | 52.97 | 384 | 0.8130 | 0.8261 |
| 0.0321 | 53.93 | 391 | 0.7682 | 0.8043 |
| 0.0321 | 54.9 | 398 | 0.8696 | 0.7826 |
| 0.0318 | 56.0 | 406 | 0.9598 | 0.8043 |
| 0.0416 | 56.97 | 413 | 0.7291 | 0.8261 |
| 0.0477 | 57.93 | 420 | 0.6869 | 0.8478 |
| 0.0477 | 58.9 | 427 | 0.7055 | 0.8478 |
| 0.0307 | 60.0 | 435 | 0.7415 | 0.8478 |
| 0.032 | 60.97 | 442 | 0.8024 | 0.8261 |
| 0.032 | 61.93 | 449 | 0.7856 | 0.8478 |
| 0.0232 | 62.9 | 456 | 0.7251 | 0.8043 |
| 0.0267 | 64.0 | 464 | 0.7231 | 0.8478 |
| 0.0456 | 64.97 | 471 | 0.7326 | 0.8696 |
| 0.0456 | 65.93 | 478 | 0.7300 | 0.8696 |
| 0.0359 | 66.9 | 485 | 0.7293 | 0.8696 |
| 0.0199 | 68.0 | 493 | 0.7361 | 0.8696 |
| 0.0235 | 68.97 | 500 | 0.7362 | 0.8696 |
| 0.0235 | 69.93 | 507 | 0.7513 | 0.8696 |
| 0.0368 | 70.9 | 514 | 0.7513 | 0.8696 |
| 0.0254 | 72.0 | 522 | 0.7586 | 0.8696 |
| 0.0254 | 72.97 | 529 | 0.7574 | 0.8696 |
| 0.029 | 73.93 | 536 | 0.7685 | 0.8478 |
| 0.0302 | 74.9 | 543 | 0.7653 | 0.8478 |
| 0.0305 | 76.0 | 551 | 0.7637 | 0.8261 |
| 0.0305 | 76.97 | 558 | 0.7645 | 0.8478 |
| 0.0301 | 77.24 | 560 | 0.7645 | 0.8478 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Krits0/KarveeSaimai | Krits0 | "2024-06-12T20:44:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xglm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-12T14:16:38Z" | Entry not found |
akaush/Autograder_V1.0 | akaush | "2024-06-12T14:16:51Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:16:51Z" | Entry not found |
trishtan/SIT330-LLM | trishtan | "2024-06-12T14:17:00Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:17:00Z" | Entry not found |
taric49/Llama3_GLLM_TEST_more_Data_points | taric49 | "2024-06-12T14:23:07Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-12T14:17:05Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** taric49
- **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)
|
facund00/alfajor | facund00 | "2024-06-12T14:18:37Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:18:37Z" | Entry not found |
trishtan/mix-translate | trishtan | "2024-06-12T14:18:38Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:18:38Z" | Entry not found |
trishtan/simple-classifier | trishtan | "2024-06-12T14:19:11Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:19:11Z" | Entry not found |
xiaol/mobius-rwkv-r6-12B | xiaol | "2024-06-16T13:43:54Z" | 0 | 1 | null | [
"arxiv:2404.05892",
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T14:19:30Z" | ---
license: apache-2.0
---
[This is a experimental model, yet it is the Most powerful RNN model in the world.](https://huggingface.co/TimeMobius/Mobius-RWKV-r6-12B)
# Mobius RWKV r6 chat 12B 16k
Mobius is a RWKV v6 arch chat model, benifit from [Matrix-Valued States and Dynamic Recurrence](https://arxiv.org/abs/2404.05892)
## Introduction
Mobius is a RWKV v6 arch model, a state based RNN+CNN+Transformer Mixed language model pretrained on a certain amount of data.
In comparison with the previous released Mobius, the improvements include:
* Only 24G Vram to run this model locally with fp16;
* Significant performance improvement in chinese;
* Stable support of 16K context length.
* function call support ;
## Usage
Chat format: User: xxxx\n\nAssistant: xxx\n\n
Recommend Temp and topp: 1 0.3
function call format example:
```
System: You are a helpful assistant with access to the following functions. Use them if required -{
"name": "get_exchange_rate",
"description": "Get the exchange rate between two currencies",
"parameters": {
"type": "object",
"properties": {
"base_currency": {
"type": "string",
"description": "The currency to convert from"
},
"target_currency": {
"type": "string",
"description": "The currency to convert to"
}
},
"required": [
"base_currency",
"target_currency"
]
}
}
User: Hi, I need to know the exchange rate from USD to EUR
Assistant: xxxx
Obersavtion: xxxx
Assistant: xxxx
```
## More details
Mobius 12B 16k based on RWKV v6 arch, which is leading state based RNN+CNN+Transformer Mixed large language model which focus opensouce community
* 10~100 trainning/inference cost reduce;
* state based,selected memory, which mean good at grok;
* community support.
## requirements
21.9G vram to run fp16, 13.7G for int8, 7.2 for nf4 with Ai00 server.
* [RWKV Runner](https://github.com/josStorer/RWKV-Runner)
* [Ai00 server](https://github.com/cgisky1980/ai00_rwkv_server)
## Benchmark
ceval 63.53
cmmlu 76.07
|
VladS159/Whisper_medium_ro_VladS_10000_steps_multi_gpu_small_lr | VladS159 | "2024-06-12T14:19:55Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:19:55Z" | Entry not found |
modelzpalace/zavychromaxl_v80 | modelzpalace | "2024-06-12T14:24:02Z" | 0 | 1 | null | [
"region:us"
] | null | "2024-06-12T14:20:45Z" | Entry not found |
TOPLXY/llama-2-7b-miniguanaco | TOPLXY | "2024-06-12T14:30:11Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-12T14:20:54Z" | Entry not found |
shuyuej/MedMistral-Spanish | shuyuej | "2024-06-12T14:34:40Z" | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T14:21:41Z" | ---
license: apache-2.0
---
|
Kostya9618/alex | Kostya9618 | "2024-06-12T14:22:29Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T14:22:29Z" | ---
license: apache-2.0
---
|
MyLo254/JUSTIN | MyLo254 | "2024-06-12T14:22:40Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-12T14:22:40Z" | ---
license: openrail
---
|
bunnybalat/Llama-2-7b-chat-mytune | bunnybalat | "2024-06-12T14:22:43Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-12T14:22:43Z" | ---
license: mit
---
|
aleoaaaa/camembert-finetuned_3336offres_oversampling | aleoaaaa | "2024-06-12T15:13:18Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"base_model:mrm8488/camembert2camembert_shared-finetuned-french-summarization",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-06-12T14:23:12Z" | ---
base_model: mrm8488/camembert2camembert_shared-finetuned-french-summarization
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: camembert2camembert_shared-finetuned-french-summarization_finetuned_12_06_14_23
results: []
---
Ce modèle a été finetuné avec 3336 + 664 offres. (Train + Validation) Le jeu de données a été oversamplé de façon à créer une répartition des classes homogène (15 par classe environ).
<!-- 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. -->
# camembert2camembert_shared-finetuned-french-summarization_finetuned_12_06_14_23
This model is a fine-tuned version of [mrm8488/camembert2camembert_shared-finetuned-french-summarization](https://huggingface.co/mrm8488/camembert2camembert_shared-finetuned-french-summarization) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2819
- Rouge1: 0.4184
- Rouge2: 0.1729
- Rougel: 0.3116
- Rougelsum: 0.3116
- Gen Len: 57.0486
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.5592 | 1.0 | 3336 | 1.2819 | 0.4184 | 0.1729 | 0.3116 | 0.3116 | 57.0486 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
dimvarsamis/llama-3-8B-claim-detection | dimvarsamis | "2024-06-12T14:23:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T14:23:19Z" | ---
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] |
taric49/Llama3_GLLM_TEST_more_Data_points_adapters | taric49 | "2024-06-12T14:27:20Z" | 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 | "2024-06-12T14:23:23Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** taric49
- **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)
|
gj09/Mistral-7B-squad2 | gj09 | "2024-06-12T15:04:02Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-06-12T14:24:26Z" | Entry not found |
pfldy2850/EEVE-Korean-Instruct-10.8B-v1.0-q4f16_1-MLC | pfldy2850 | "2024-06-12T14:43:33Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T14:25:47Z" | ---
license: apache-2.0
---
|
Tencent-Hunyuan/HYDiT-LoRA | Tencent-Hunyuan | "2024-06-19T13:14:18Z" | 0 | 6 | null | [
"safetensors",
"en",
"license:other",
"region:us"
] | null | "2024-06-12T14:25:47Z" | ---
license: other
license_name: tencent-hunyuan-community
license_link: https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/blob/main/LICENSE.txt
language:
- en
---
# HunyuanDiT LoRA
Language: **English**
## Instructions
The dependencies and installation are basically the same as the [**original model**](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT-v1.1).
We provide two types of trained LoRA weights for you to test.
Then download the model using the following commands:
```bash
cd HunyuanDiT
# Use the huggingface-cli tool to download the model.
huggingface-cli download Tencent-Hunyuan/HYDiT-LoRA --local-dir ./ckpts/t2i/lora
```
## Training
We provide three types of weights for fine-tuning HY-DiT LoRA, `ema`, `module` and `distill`, and you can choose according to the actual effect. By default, we use `ema` weights.
Here is an example, we load the `ema` weights into the main model and perform LoRA fine-tuning through the `--ema-to-module` parameter.
If you want to load the `module` weights into the main model, just remove the `--ema-to-module` parameter.
If multiple resolution are used, you need to add the `--multireso` and `--reso-step 64 ` parameter.
```bash
model='DiT-g/2' # model type
task_flag="lora_porcelain_ema_rank64" # task flag
resume=./ckpts/t2i/model/ # resume checkpoint
index_file=dataset/porcelain/jsons/porcelain.json # the selected data indices
results_dir=./log_EXP # save root for results
batch_size=1 # training batch size
image_size=1024 # training image resolution
grad_accu_steps=2 # gradient accumulation steps
warmup_num_steps=0 # warm-up steps
lr=0.0001 # learning rate
ckpt_every=100 # create a ckpt every a few steps.
ckpt_latest_every=2000 # create a ckpt named `latest.pt` every a few steps.
rank=64 # rank of lora
max_training_steps=2000 # Maximum training iteration steps
PYTHONPATH=./ deepspeed hydit/train_deepspeed.py \
--task-flag ${task_flag} \
--model ${model} \
--training_parts lora \
--rank ${rank} \
--resume-split \
--resume ${resume} \
--ema-to-module \
--lr ${lr} \
--noise-schedule scaled_linear --beta-start 0.00085 --beta-end 0.03 \
--predict-type v_prediction \
--uncond-p 0.44 \
--uncond-p-t5 0.44 \
--index-file ${index_file} \
--random-flip \
--batch-size ${batch_size} \
--image-size ${image_size} \
--global-seed 999 \
--grad-accu-steps ${grad_accu_steps} \
--warmup-num-steps ${warmup_num_steps} \
--use-flash-attn \
--use-fp16 \
--ema-dtype fp32 \
--results-dir ${results_dir} \
--ckpt-every ${ckpt_every} \
--max-training-steps ${max_training_steps}\
--ckpt-latest-every ${ckpt_latest_every} \
--log-every 10 \
--deepspeed \
--deepspeed-optimizer \
--use-zero-stage 2 \
--qk-norm \
--rope-img base512 \
--rope-real \
"$@"
```
Recommended parameter settings
| Parameter | Description | Recommended Parameter Value | Note|
|:---------------:|:---------:|:---------------------------------------------------:|:--:|
| `--batch_size` | Training batch size | 1 | Depends on GPU memory|
| `--grad-accu-steps` | Size of gradient accumulation | 2 | - |
| `--rank` | Rank of lora | 64 | Choosing from 8-128|
| `--max-training-steps` | Training steps | 2000 | Depend on training data size, for reference apply 2000 steps on 100 images|
| `--lr` | Learning rate | 0.0001 | - |
## Inference
### Using Gradio
Make sure you have activated the conda environment before running the following command.
> ⚠️ Important Reminder:
> We recommend not using prompt enhance, as it may lead to the disappearance of style words.
```shell
# jade style
# By default, we start a Chinese UI.
python app/hydit_app.py --load-key ema --lora_ckpt ./ckpts/t2i/lora/jade
# Using Flash Attention for acceleration.
python app/hydit_app.py --infer-mode fa --load-key ema --lora_ckpt ./ckpts/t2i/lora/jade
# You can disable the enhancement model if the GPU memory is insufficient.
# The enhancement will be unavailable until you restart the app without the `--no-enhance` flag.
python app/hydit_app.py --no-enhance --load-key ema --lora_ckpt ./ckpts/t2i/lora/jade
# Start with English UI
python app/hydit_app.py --lang en --load-key ema --lora_ckpt ./ckpts/t2i/lora/jade
# porcelain style
# By default, we start a Chinese UI.
python app/hydit_app.py --load-key ema --lora_ckpt ./ckpts/t2i/lora/porcelain
# Using Flash Attention for acceleration.
python app/hydit_app.py --infer-mode fa --load-key ema --lora_ckpt ./ckpts/t2i/lora/porcelain
# You can disable the enhancement model if the GPU memory is insufficient.
# The enhancement will be unavailable until you restart the app without the `--no-enhance` flag.
python app/hydit_app.py --no-enhance --load-key ema --lora_ckpt ./ckpts/t2i/lora/porcelain
# Start with English UI
python app/hydit_app.py --lang en --load-key ema --lora_ckpt ./ckpts/t2i/lora/porcelain
```
### Using Command Line
We provide several commands to quick start:
```shell
# jade style
# Prompt Enhancement + Text-to-Image. Torch mode
python sample_t2i.py --prompt "玉石绘画风格，一只猫在追蝴蝶" --load-key ema --lora_ckpt ./ckpts/t2i/lora/jade
# Only Text-to-Image. Torch mode
python sample_t2i.py --prompt "玉石绘画风格，一只猫在追蝴蝶" --no-enhance --load-key ema --lora_ckpt ./ckpts/t2i/lora/jade
# Only Text-to-Image. Flash Attention mode
python sample_t2i.py --infer-mode fa --prompt "玉石绘画风格，一只猫在追蝴蝶" --load-key ema --lora_ckpt ./ckpts/t2i/lora/jade
# Generate an image with other image sizes.
python sample_t2i.py --prompt "玉石绘画风格，一只猫在追蝴蝶" --image-size 1280 768 --load-key ema --lora_ckpt ./ckpts/t2i/lora/jade
# porcelain style
# Prompt Enhancement + Text-to-Image. Torch mode
python sample_t2i.py --prompt "青花瓷风格，一只猫在追蝴蝶" --load-key ema --lora_ckpt ./ckpts/t2i/lora/porcelain
# Only Text-to-Image. Torch mode
python sample_t2i.py --prompt "青花瓷风格，一只猫在追蝴蝶" --no-enhance --load-key ema --lora_ckpt ./ckpts/t2i/lora/porcelain
# Only Text-to-Image. Flash Attention mode
python sample_t2i.py --infer-mode fa --prompt "青花瓷风格，一只猫在追蝴蝶" --load-key ema --lora_ckpt ./ckpts/t2i/lora/porcelain
# Generate an image with other image sizes.
python sample_t2i.py --prompt "青花瓷风格，一只猫在追蝴蝶" --image-size 1280 768 --load-key ema --lora_ckpt ./ckpts/t2i/lora/porcelain
```
Regarding how to use the LoRA weights we trained in diffusion, we provide the following script. To ensure compatibility with the diffuser, some modifications are made, which means that LoRA cannot be directly loaded.
```python
import torch
from diffusers import HunyuanDiTPipeline
num_layers = 40
def load_hunyuan_dit_lora(transformer_state_dict, lora_state_dict, lora_scale):
for i in range(num_layers):
Wqkv = torch.matmul(lora_state_dict[f"blocks.{i}.attn1.Wqkv.lora_B.weight"], lora_state_dict[f"blocks.{i}.attn1.Wqkv.lora_A.weight"])
q, k, v = torch.chunk(Wqkv, 3, dim=0)
transformer_state_dict[f"blocks.{i}.attn1.to_q.weight"] += lora_scale * q
transformer_state_dict[f"blocks.{i}.attn1.to_k.weight"] += lora_scale * k
transformer_state_dict[f"blocks.{i}.attn1.to_v.weight"] += lora_scale * v
out_proj = torch.matmul(lora_state_dict[f"blocks.{i}.attn1.out_proj.lora_B.weight"], lora_state_dict[f"blocks.{i}.attn1.out_proj.lora_A.weight"])
transformer_state_dict[f"blocks.{i}.attn1.to_out.0.weight"] += lora_scale * out_proj
q_proj = torch.matmul(lora_state_dict[f"blocks.{i}.attn2.q_proj.lora_B.weight"], lora_state_dict[f"blocks.{i}.attn2.q_proj.lora_A.weight"])
transformer_state_dict[f"blocks.{i}.attn2.to_q.weight"] += lora_scale * q_proj
kv_proj = torch.matmul(lora_state_dict[f"blocks.{i}.attn2.kv_proj.lora_B.weight"], lora_state_dict[f"blocks.{i}.attn2.kv_proj.lora_A.weight"])
k, v = torch.chunk(kv_proj, 2, dim=0)
transformer_state_dict[f"blocks.{i}.attn2.to_k.weight"] += lora_scale * k
transformer_state_dict[f"blocks.{i}.attn2.to_v.weight"] += lora_scale * v
out_proj = torch.matmul(lora_state_dict[f"blocks.{i}.attn2.out_proj.lora_B.weight"], lora_state_dict[f"blocks.{i}.attn2.out_proj.lora_A.weight"])
transformer_state_dict[f"blocks.{i}.attn2.to_out.0.weight"] += lora_scale * out_proj
q_proj = torch.matmul(lora_state_dict["pooler.q_proj.lora_B.weight"], lora_state_dict["pooler.q_proj.lora_A.weight"])
transformer_state_dict["time_extra_emb.pooler.q_proj.weight"] += lora_scale * q_proj
return transformer_state_dict
pipe = HunyuanDiTPipeline.from_pretrained("Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers", torch_dtype=torch.float16)
pipe.to("cuda")
from safetensors import safe_open
lora_state_dict = {}
with safe_open("./ckpts/t2i/lora/jade/adapter_model.safetensors", framework="pt", device=0) as f:
for k in f.keys():
lora_state_dict[k[17:]] = f.get_tensor(k) # remove 'basemodel.model'
transformer_state_dict = pipe.transformer.state_dict()
transformer_state_dict = load_hunyuan_dit_lora(transformer_state_dict, lora_state_dict, lora_scale=1.0)
pipe.transformer.load_state_dict(transformer_state_dict)
prompt = "玉石绘画风格，一只猫在追蝴蝶"
image = pipe(
prompt,
num_inference_steps=100,
guidance_scale=6.0,
).images[0]
image.save('img.png')
```
More example prompts can be found in [example_prompts.txt](example_prompts.txt)
|
scientisthere/mm | scientisthere | "2024-06-12T14:26:56Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | "2024-06-12T14:26:12Z" | ---
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
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### Direct Use
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[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
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## Bias, Risks, and Limitations
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[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
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[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
[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. -->
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## Model Card Contact
[More Information Needed] |
bilalkakar/BLOOM560-MCQ-Quantized | bilalkakar | "2024-06-12T14:29:15Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bloom",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"gptq",
"region:us"
] | text-generation | "2024-06-12T14:27:47Z" | Entry not found |
Finit00/Tst | Finit00 | "2024-06-12T14:28:15Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T14:28:15Z" | ---
license: apache-2.0
---
|
ar9av/phi-finetuned-xaxis2 | ar9av | "2024-06-13T04:01:58Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"phi3_v",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | text-generation | "2024-06-12T14:28:48Z" | ---
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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[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]
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## Model Card Contact
[More Information Needed] |
polyconnect/Reinforce-Pixelcopter-PLE-v0 | polyconnect | "2024-06-15T18:24:38Z" | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-12T14:28:49Z" | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 43.30 +/- 30.27
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
an11/111 | an11 | "2024-06-12T14:30:55Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:30:55Z" | Entry not found |
Veture/merged_quanto_dpo | Veture | "2024-06-12T14:31:17Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:31:17Z" | Entry not found |
DenisSpecialist/MySex | DenisSpecialist | "2024-06-12T14:32:29Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-12T14:32:29Z" | ---
license: mit
---
|
lukasbraach/langdecoder_rwth_pretrain | lukasbraach | "2024-06-12T16:12:11Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"speech_to_text_2",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T14:32:53Z" | ---
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] |
fiveit/test | fiveit | "2024-06-12T14:33:18Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T14:33:17Z" | ---
license: apache-2.0
---
|
Hdvvvid/Wad | Hdvvvid | "2024-06-12T14:34:13Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T14:34:13Z" | ---
license: apache-2.0
---
|
acl-srw-2024/phi3-14b-unsloth-sft-quip-3bit-pt | acl-srw-2024 | "2024-06-12T14:53:54Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:35:53Z" | Entry not found |
jurieyel/77cdm-sqlcoder-8b-2 | jurieyel | "2024-06-12T14:37:01Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:defog/sqlcoder-7b-2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T14:36:50Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: defog/sqlcoder-7b-2
---
# Uploaded model
- **Developed by:** jurieyel
- **License:** apache-2.0
- **Finetuned from model :** defog/sqlcoder-7b-2
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)
|
magniolia/LLM | magniolia | "2024-06-12T14:38:47Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:38:47Z" | Entry not found |
DBangshu/GPT2_1_5 | DBangshu | "2024-06-12T14:42:21Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-12T14:41:58Z" | ---
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.
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Ahmed107/test_v1245 | Ahmed107 | "2024-06-12T14:43:03Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:43:03Z" | Entry not found |
Augusto777/vit-base-patch16-224-ve-U16-b-80 | Augusto777 | "2024-06-12T15:08:16Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-12T14:43:04Z" | ---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-ve-U16-b-80
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8695652173913043
---
<!-- 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. -->
# vit-base-patch16-224-ve-U16-b-80
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5265
- Accuracy: 0.8696
## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 80
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | 1.3828 | 0.4565 |
| 1.3846 | 2.0 | 16 | 1.3610 | 0.5 |
| 1.3611 | 3.0 | 24 | 1.2967 | 0.4348 |
| 1.2759 | 4.0 | 32 | 1.1830 | 0.3913 |
| 1.1164 | 5.0 | 40 | 1.0824 | 0.3696 |
| 1.1164 | 6.0 | 48 | 0.9665 | 0.5 |
| 0.98 | 7.0 | 56 | 0.9036 | 0.5652 |
| 0.8533 | 8.0 | 64 | 0.8348 | 0.7826 |
| 0.7321 | 9.0 | 72 | 0.7397 | 0.8261 |
| 0.6075 | 10.0 | 80 | 0.7155 | 0.7174 |
| 0.6075 | 11.0 | 88 | 0.6006 | 0.8261 |
| 0.4901 | 12.0 | 96 | 0.5265 | 0.8696 |
| 0.3967 | 13.0 | 104 | 0.5214 | 0.8043 |
| 0.2746 | 14.0 | 112 | 0.5433 | 0.7826 |
| 0.2366 | 15.0 | 120 | 0.6141 | 0.7826 |
| 0.2366 | 16.0 | 128 | 0.6658 | 0.7826 |
| 0.2247 | 17.0 | 136 | 0.6327 | 0.7609 |
| 0.2047 | 18.0 | 144 | 0.5339 | 0.8261 |
| 0.1592 | 19.0 | 152 | 0.6647 | 0.8043 |
| 0.1349 | 20.0 | 160 | 0.7483 | 0.7609 |
| 0.1349 | 21.0 | 168 | 0.7387 | 0.8043 |
| 0.1285 | 22.0 | 176 | 0.8261 | 0.7609 |
| 0.1104 | 23.0 | 184 | 0.7151 | 0.8043 |
| 0.1191 | 24.0 | 192 | 0.7785 | 0.7609 |
| 0.1074 | 25.0 | 200 | 0.8902 | 0.7391 |
| 0.1074 | 26.0 | 208 | 0.7757 | 0.7826 |
| 0.0947 | 27.0 | 216 | 0.7157 | 0.7826 |
| 0.0973 | 28.0 | 224 | 0.8198 | 0.7826 |
| 0.0992 | 29.0 | 232 | 0.7240 | 0.8261 |
| 0.0766 | 30.0 | 240 | 0.6993 | 0.8043 |
| 0.0766 | 31.0 | 248 | 0.5688 | 0.8261 |
| 0.0606 | 32.0 | 256 | 0.6202 | 0.8478 |
| 0.0633 | 33.0 | 264 | 0.6740 | 0.8261 |
| 0.0681 | 34.0 | 272 | 0.6782 | 0.8261 |
| 0.0591 | 35.0 | 280 | 0.8370 | 0.7826 |
| 0.0591 | 36.0 | 288 | 0.6995 | 0.8261 |
| 0.0731 | 37.0 | 296 | 0.7560 | 0.8261 |
| 0.0618 | 38.0 | 304 | 0.6730 | 0.8261 |
| 0.0543 | 39.0 | 312 | 0.7166 | 0.8261 |
| 0.0574 | 40.0 | 320 | 0.7332 | 0.8261 |
| 0.0574 | 41.0 | 328 | 0.6982 | 0.8261 |
| 0.0707 | 42.0 | 336 | 0.7183 | 0.7826 |
| 0.0646 | 43.0 | 344 | 0.7568 | 0.8043 |
| 0.0476 | 44.0 | 352 | 0.8521 | 0.8043 |
| 0.047 | 45.0 | 360 | 0.8992 | 0.8043 |
| 0.047 | 46.0 | 368 | 0.8749 | 0.7826 |
| 0.0406 | 47.0 | 376 | 0.9928 | 0.7826 |
| 0.0361 | 48.0 | 384 | 0.9659 | 0.7826 |
| 0.042 | 49.0 | 392 | 0.8839 | 0.8043 |
| 0.0421 | 50.0 | 400 | 0.8613 | 0.7391 |
| 0.0421 | 51.0 | 408 | 0.9006 | 0.7826 |
| 0.0396 | 52.0 | 416 | 0.8627 | 0.7826 |
| 0.0255 | 53.0 | 424 | 0.8717 | 0.7609 |
| 0.0359 | 54.0 | 432 | 1.0508 | 0.7609 |
| 0.0424 | 55.0 | 440 | 0.9745 | 0.7826 |
| 0.0424 | 56.0 | 448 | 0.9511 | 0.8043 |
| 0.0364 | 57.0 | 456 | 0.9239 | 0.8043 |
| 0.0444 | 58.0 | 464 | 0.9500 | 0.7826 |
| 0.0445 | 59.0 | 472 | 0.9266 | 0.8261 |
| 0.0368 | 60.0 | 480 | 0.9346 | 0.8043 |
| 0.0368 | 61.0 | 488 | 0.9513 | 0.8043 |
| 0.0278 | 62.0 | 496 | 0.9505 | 0.8043 |
| 0.0324 | 63.0 | 504 | 0.9625 | 0.8261 |
| 0.0308 | 64.0 | 512 | 0.9720 | 0.8261 |
| 0.0185 | 65.0 | 520 | 0.9515 | 0.8043 |
| 0.0185 | 66.0 | 528 | 0.9278 | 0.8043 |
| 0.0323 | 67.0 | 536 | 0.9315 | 0.8261 |
| 0.0251 | 68.0 | 544 | 0.9794 | 0.8043 |
| 0.0297 | 69.0 | 552 | 1.0378 | 0.7609 |
| 0.0257 | 70.0 | 560 | 1.0336 | 0.7609 |
| 0.0257 | 71.0 | 568 | 1.0577 | 0.7826 |
| 0.02 | 72.0 | 576 | 1.0332 | 0.8043 |
| 0.0226 | 73.0 | 584 | 1.0165 | 0.8043 |
| 0.0257 | 74.0 | 592 | 1.0194 | 0.8043 |
| 0.0232 | 75.0 | 600 | 1.0026 | 0.8043 |
| 0.0232 | 76.0 | 608 | 1.0073 | 0.8043 |
| 0.0274 | 77.0 | 616 | 1.0099 | 0.8043 |
| 0.0182 | 78.0 | 624 | 1.0170 | 0.8043 |
| 0.0375 | 79.0 | 632 | 1.0139 | 0.8043 |
| 0.029 | 80.0 | 640 | 1.0128 | 0.8043 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
freyza/world | freyza | "2024-06-12T14:45:31Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:43:29Z" | Entry not found |
ibayoussef/AlwassitAi | ibayoussef | "2024-06-12T14:44:33Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-12T14:44:33Z" | ---
license: mit
---
|
LarryAIDraw/Char_Honkai_Bronya_Pony_v1 | LarryAIDraw | "2024-06-12T14:47:55Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2024-06-12T14:45:40Z" | ---
license: creativeml-openrail-m
---
https://civitai.com/models/130516?modelVersionId=568038 |
LarryAIDraw/chiori-giPO-v1a | LarryAIDraw | "2024-06-12T14:48:06Z" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2024-06-12T14:46:03Z" | ---
license: creativeml-openrail-m
---
https://civitai.com/models/244880/sd15-and-pony-genshin-impact-chiori-or-or |
richardkelly/google-gemma-2b-1718203583 | richardkelly | "2024-06-12T17:07:27Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-12T14:46:24Z" | ---
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.
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[More Information Needed]
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alexred7/vulnerabilty-classification-14-llama-2-7b_dataset_one_more_epoch | alexred7 | "2024-06-12T14:55:06Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-12T14:46:47Z" | Entry not found |
richardkelly/google-gemma-2b-1718203726 | richardkelly | "2024-06-12T17:07:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-12T14:48:48Z" | ---
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. -->
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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
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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taric49/LLAMA3_MoRA_1_adaptors | taric49 | "2024-06-12T14:49:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T14:48:49Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- 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] |
GrafikXxxxxxxYyyyyyyyyyy/sd15_AnyLoRA | GrafikXxxxxxxYyyyyyyyyyy | "2024-06-12T14:52:35Z" | 0 | 0 | diffusers | [
"diffusers",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-06-12T14:50:04Z" | Entry not found |
carlisleking/dqn-SpaceInvadersNoFrameskip-v4 | carlisleking | "2024-06-12T14:52:06Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:52:06Z" | Entry not found |
taric49/Llama3_GLLM_TEST-3 | taric49 | "2024-06-12T14:59:40Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-12T14:52:31Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** taric49
- **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)
|
Cannon07/Test | Cannon07 | "2024-06-12T14:52:42Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-12T14:52:42Z" | ---
license: mit
---
|
shakun42/indic-bert-finetuned-squad1.3 | shakun42 | "2024-06-12T14:52:49Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:52:49Z" | Entry not found |
Himech/outputs | Himech | "2024-06-12T14:54:05Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:54:05Z" | Entry not found |
MinnieTang/NotaGPT-7B | MinnieTang | "2024-06-12T15:34:29Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llava_llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-12T14:54:23Z" | Entry not found |
Wauplin/keras_image_classification_example | Wauplin | "2024-06-12T15:31:01Z" | 0 | 1 | keras | [
"keras",
"tensorflow",
"torch",
"jax",
"image-classification",
"region:us"
] | image-classification | "2024-06-12T14:54:47Z" | ---
library_name: keras
pipeline_tag: image-classification
tags:
- tensorflow
- torch
- jax
---
This model has been uploaded using the Keras library and can be used with JAX, TensorFlow, and PyTorch backends.
This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information.
Here is a visual representation of the model config. For more details, check out [config.json](./config.json).
|
cocktailpeanut/3ds | cocktailpeanut | "2024-06-12T16:16:47Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T14:55:32Z" | ---
license: apache-2.0
---
|
Fahim22/New_Model | Fahim22 | "2024-06-12T14:56:21Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-12T14:56:21Z" | ---
license: openrail
---
|
yraziel/yinon_magal | yraziel | "2024-06-12T14:58:44Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:57:07Z" | Entry not found |
srdd/kk | srdd | "2024-06-12T14:57:55Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:57:55Z" | Entry not found |
nikipiki/rrrr | nikipiki | "2024-06-12T14:58:20Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T14:58:20Z" | Entry not found |
IEETA/RobertaMultiHeadCRF-C32-1 | IEETA | "2024-06-12T16:22:13Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"crf-tagger",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | "2024-06-12T14:59:39Z" | ---
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] |
DaenerysTg/q-FrozenLake-v1-4x4-noSlippery | DaenerysTg | "2024-06-12T14:59:54Z" | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-12T14:59:51Z" | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="DaenerysTg/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
taric49/Llama3_GLLM_TEST-3_adaptors | taric49 | "2024-06-12T15:02:22Z" | 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 | "2024-06-12T14:59:53Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** taric49
- **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)
|
NangTherr/output_dir | NangTherr | "2024-06-12T15:25:41Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"translation",
"generated_from_trainer",
"base_model:VietAI/envit5-translation",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | translation | "2024-06-12T15:04:24Z" | ---
license: openrail
base_model: VietAI/envit5-translation
tags:
- translation
- generated_from_trainer
model-index:
- name: output_dir
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. -->
# output_dir
This model is a fine-tuned version of [VietAI/envit5-translation](https://huggingface.co/VietAI/envit5-translation) 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
nicher92/tts_1.3b | nicher92 | "2024-06-12T15:16:16Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T15:05:19Z" | Entry not found |
wshuo/llama-7b-layer-ft | wshuo | "2024-06-12T15:08:01Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T15:08:01Z" | Entry not found |
nithiyn/codestral-neuron | nithiyn | "2024-06-12T15:08:05Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T15:08:05Z" | ---
license: apache-2.0
---
|