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1231czx/2b_dpo_iter1_400_step_sft2epoch_merged_math_gsm8k | 1231czx | "2024-06-24T01:16:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-24T01:14:18Z" | ---
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
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#### 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]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
rohit5895/distilbert-base-uncased-finetuned-imdb | rohit5895 | "2024-06-24T03:45:29Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2024-06-24T01:18:11Z" | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0092
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.3996 | 1.0 | 157 | 3.1337 |
| 3.1913 | 2.0 | 314 | 3.0840 |
| 3.1346 | 3.0 | 471 | 3.0092 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
PrunaAI/maywell-Mistral-ko-7B-v0.1-AWQ-4bit-smashed | PrunaAI | "2024-06-24T01:21:58Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"pruna-ai",
"base_model:maywell/Mistral-ko-7B-v0.1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | "2024-06-24T01:19:51Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: maywell/Mistral-ko-7B-v0.1
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 awq.
- ***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 maywell/Mistral-ko-7B-v0.1 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install autoawq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from awq import AutoAWQForCausalLM
model = AutoAWQForCausalLM.from_quantized("PrunaAI/maywell-Mistral-ko-7B-v0.1-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("maywell/Mistral-ko-7B-v0.1")
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 maywell/Mistral-ko-7B-v0.1 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). |
CHARKA/Meta-Llama-3-8B-InstructSMALLDATAPEDAG | CHARKA | "2024-06-24T01:19:58Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T01:19:58Z" | Entry not found |
noanabeshima/tiny_model | noanabeshima | "2024-06-25T00:04:05Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-24T01:19:59Z" | ---
license: mit
---
See https://github.com/noanabeshima/tiny_model
TinyModel is trained for 3 epochs on https://huggingface.co/datasets/noanabeshima/TinyStoriesV2 |
jlee6741/Llama3-MIMIC | jlee6741 | "2024-06-24T01:22:59Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T01:22:59Z" | Entry not found |
JoshuaChak/Meta-Chameleon | JoshuaChak | "2024-06-24T01:41:50Z" | 0 | 0 | null | [
"arxiv:2405.09818",
"region:us"
] | null | "2024-06-24T01:23:53Z" | # Mixed-modal and Text-only Prompts for Human Evaluation
This file ```prompts_for_human_evaluations.jsonl``` contains the 1,048 prompts used for evaluating Chameleon's output: 441 (42.1%) are mixed-modal (i.e., containing both text and images), and the remaining 607 (57.9%) are text-only. The expected responses are mixed-modal, containing both text and images.
## Background
We work with a third-party crowdsourcing vendor to collect a set of diverse and natural prompts from human annotators. Specifically, we ask annotators to creatively think about what they want a multi-modal model to generate for different real-life scenarios. For example, for the scenario of “imagine you are in a kitchen”, annotators may come up with prompts like “How to cook pasta?” or “How should I design the layout of my island? Show me some examples.” The prompts can be text-only or text with some images, and the expected responses should be mixed-modal, containing both text and images.
After collecting an initial set of prompts, we ask three random annotators to evaluate whether the prompts are clear and whether they expect the responses to contain images. We use a majority vote to filter unclear prompts and prompts that don’t expect mixed-modal responses. In the end, our final evaluation set contains
1,048 prompts: 441 (42.1%) are mixed-modal (i.e., containing both text and images), and the remaining 607 (57.9%) are text-only.
More details on how these prompts are collected and some statistics can be found in the [paper](https://arxiv.org/pdf/2405.09818).
## File format
Each line of the file ```prompts_for_human_evaluations.jsonl``` defines a prompt, with the following fields:
- ```id```: The GUID of this prompt.
- ```prompt```: The prompt content. If the prompt contains images, then their position is given by the special ```<img>``` token.
- ```task_type```: The task category of this prompt.
- ```image_urls```: A list of the URLs of images used in the prompts. Each image maps to a special ```<img>``` token in the prompt by order.
|
pathlighter/mistral_sharegpt_echo_batch_2-ep-4 | pathlighter | "2024-06-24T01:26:30Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T01:26:30Z" | Entry not found |
PrunaAI/vilm-vinallama-7b-chat-AWQ-4bit-smashed | PrunaAI | "2024-06-24T01:35:06Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pruna-ai",
"conversational",
"base_model:vilm/vinallama-7b-chat",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | "2024-06-24T01:33:17Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: vilm/vinallama-7b-chat
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 awq.
- ***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 vilm/vinallama-7b-chat installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install autoawq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from awq import AutoAWQForCausalLM
model = AutoAWQForCausalLM.from_quantized("PrunaAI/vilm-vinallama-7b-chat-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("vilm/vinallama-7b-chat")
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 vilm/vinallama-7b-chat 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). |
SplaatCsupo/DrKlaskyCsupo | SplaatCsupo | "2024-06-24T01:34:07Z" | 0 | 1 | null | [
"license:cc",
"region:us"
] | null | "2024-06-24T01:33:50Z" | ---
license: cc
---
|
SplaatCsupo/DrKlaskyCsupoSpanish | SplaatCsupo | "2024-06-24T01:34:36Z" | 0 | 1 | null | [
"license:cc0-1.0",
"region:us"
] | null | "2024-06-24T01:34:22Z" | ---
license: cc0-1.0
---
|
bigstorm/firefunction-v2-6.0bpw-8hb-exl2 | bigstorm | "2024-06-24T01:58:20Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"function-calling",
"conversational",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"6-bit",
"exl2",
"region:us"
] | text-generation | "2024-06-24T01:38:03Z" | ---
license: llama3
tags:
- function-calling
---
# BigStorm - ExLLamaV2 (Exl2) Quantization
- 6.0 bpw target
- 8 head bits
Enjoy! Raise an issue if you'd like other BPW levels.
#### Base Model Card Follows:
---
# FireFunction V2: Fireworks Function Calling Model
[**Try on Fireworks**](https://fireworks.ai/models/fireworks/firefunction-v2) | [**API Docs**](https://readme.fireworks.ai/docs/function-calling) | [**Demo App**](https://functional-chat.vercel.app/) | [**Discord**](https://discord.gg/mMqQxvFD9A)
<img src="https://cdn-uploads.huggingface.co/production/uploads/64b6f3a72f5a966b9722de88/nJNtxLzWswBDKK1iOZblb.png" alt="firefunction" width="400"/>
FireFunction is a state-of-the-art function calling model with a commercially viable license. View detailed info in our [announcement blog](https://fireworks.ai/blog/firefunction-v2-launch-post). Key info and highlights:
**Comparison with other models:**
- Competitive with GPT-4o at function-calling, scoring 0.81 vs 0.80 on a medley of public evaluations
- Trained on Llama 3 and retains Llama 3’s conversation and instruction-following capabilities, scoring 0.84 vs Llama 3’s 0.89 on MT bench
- Significant quality improvements over FireFunction v1 across the broad range of metrics
**General info:**
🐾 Successor of the [FireFunction](https://fireworks.ai/models/fireworks/firefunction-v1) model
🔆 Support of parallel function calling (unlike FireFunction v1) and good instruction following
💡 Hosted on the [Fireworks](https://fireworks.ai/models/fireworks/firefunction-v2) platform at < 10% of the cost of GPT 4o and 2x the speed
## Intended Use and Limitations
### Supported usecases
The model was tuned to perfom well on a range of usecases including:
* general instruction following
* multi-turn chat mixing vanilla messages with function calls
* single- and parallel function calling
* up to 20 function specs supported at once
* structured information extraction
The model has an 8k context window, like Llama 3
### Out-of-Scope Use
The model was not optimized for the following use cases:
* 100+ function specs
* nested function calling
## Metrics
| Benchmark | Firefunction v1 | Firefunction v2 | Llama 3 70b Instruct | Gpt-4o |
|:-----------------------------------|:----------------|:----------------|:---------------------|:-------|
| Gorilla simple | 0.91 | 0.94 | 0.925 | 0.88 |
| Gorilla multiple_function | 0.92 | 0.91 | 0.86 | 0.91 |
| Gorilla parallel_function | 0 | 0.9 | 0.86 | 0.89 |
| Gorilla parallel_multiple_function | 0 | 0.8 | 0.615 | 0.72 |
| Nexus parallel | 0.38 | 0.53 | 0.3 | 0.47 |
| Mtbench | 0.73 | 0.84 | 0.89 | 0.93 |
| Average | 0.49 | 0.82 | 0.74 | 0.8 |
## Example Usage
See [documentation](https://readme.fireworks.ai/docs/function-calling) for more detail.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import json
from datetime import datetime
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("fireworks-ai/firefunction-v2", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("fireworks-ai/firefunction-v2")
function_spec = [
{
"name": "get_stock_price",
"description": "Get the current stock price",
"parameters": {
"type": "object",
"properties": {
"symbol": {
"type": "string",
"description": "The stock symbol, e.g. AAPL, GOOG"
}
},
"required": [
"symbol"
]
}
},
{
"name": "check_word_anagram",
"description": "Check if two words are anagrams of each other",
"parameters": {
"type": "object",
"properties": {
"word1": {
"type": "string",
"description": "The first word"
},
"word2": {
"type": "string",
"description": "The second word"
}
},
"required": [
"word1",
"word2"
]
}
}
]
functions = json.dumps(function_spec, indent=4)
messages = [
{'role': 'system', 'content': 'You are a helpful assistant with access to functions. Use them if required.'},
{'role': 'user', 'content': 'Hi, can you tell me the current stock price of google and netflix?'}
]
now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
model_inputs = tokenizer.apply_chat_template(messages, functions=functions, datetime=now, return_tensors="pt").to(model.device)
generated_ids = model.generate(model_inputs, max_new_tokens=128)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Resources
* [Fireworks discord with function calling channel](https://discord.gg/mMqQxvFD9A)
* [Documentation](https://readme.fireworks.ai/docs/function-calling)
* [Demo app](https://functional-chat.vercel.app/)
* [Try in Fireworks prompt playground UI](https://fireworks.ai/models/fireworks/firefunction-v2) |
jiabing24/oxford-pet-segmentation | jiabing24 | "2024-07-02T12:36:11Z" | 0 | 0 | segmentation-models-pytorch | [
"segmentation-models-pytorch",
"safetensors",
"semantic-segmentation",
"pytorch",
"image-segmentation",
"license:mit",
"region:us"
] | image-segmentation | "2024-06-24T01:38:37Z" | ---
library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
- semantic-segmentation
- pytorch
- segmentation-models-pytorch
languages:
- python
---
# FPN Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.FPN.from_pretrained("oxford-pet-segmentation")
```
## Model init parameters
```python
model_init_params = {
"encoder_name": "resnet34",
"encoder_depth": 5,
"encoder_weights": "imagenet",
"decoder_pyramid_channels": 256,
"decoder_segmentation_channels": 128,
"decoder_merge_policy": "add",
"decoder_dropout": 0.2,
"in_channels": 4,
"classes": 1,
"activation": None,
"upsampling": 4,
"aux_params": None
}
```
## Model metrics
```json
[
{
"test_per_image_iou": 0.6289815902709961,
"test_dataset_iou": 0.7612584233283997
}
]
```
## Dataset
Dataset name: GID
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) |
lance0145/t2a | lance0145 | "2024-06-25T02:40:40Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T01:39:48Z" | ---
title: t2a
emoji: 💻
colorFrom: indigo
colorTo: yellow
sdk: gradio
sdk_version: 4.15.0
app_file: app.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
KuhaiAI/Rodmo | KuhaiAI | "2024-06-24T01:39:58Z" | 0 | 0 | null | [
"license:unknown",
"region:us"
] | null | "2024-06-24T01:39:58Z" | ---
license: unknown
---
|
Thvkoo/RVCmodels | Thvkoo | "2024-06-24T01:49:00Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-24T01:44:32Z" | ---
license: openrail
---
|
yabichiu/llava_7b-v1.6-vicuna-q8_0_Ollama | yabichiu | "2024-06-24T01:50:57Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-24T01:44:48Z" | ---
license: apache-2.0
---
|
Geneva/Llama-2-7b-finetune-Databaseset | Geneva | "2024-06-24T02:27:54Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-24T01:46:10Z" | Entry not found |
Frixi/300_Leonidas_2006 | Frixi | "2024-06-24T01:52:38Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-24T01:49:27Z" | ---
license: openrail
---
|
DBangshu/gemma_e5_0_1 | DBangshu | "2024-06-24T01:57:57Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-24T01:55:35Z" | ---
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] |
ar9av/paligemma_vqav2_ft | ar9av | "2024-06-24T03:12:50Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-06-24T01:56:38Z" | Entry not found |
AlignmentResearch/robust_llm_pythia-12b-imdb-ian-nd | AlignmentResearch | "2024-06-24T02:03:44Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-classification | "2024-06-24T01:57:14Z" | Entry not found |
yabichiu/llava_7b-v1.6-mistral-q8_0_Ollama | yabichiu | "2024-06-24T03:49:29Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-24T01:58:37Z" | ---
license: apache-2.0
---
|
surya-narayanan/psychology | surya-narayanan | "2024-06-24T04:53:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-24T02:07:47Z" | ---
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]
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## Model Card Contact
[More Information Needed] |
An24/wav2vec2-large-xls-r-vi-colab | An24 | "2024-06-24T02:26:46Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-24T02:08:13Z" | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-vi-colab
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. -->
# wav2vec2-large-xls-r-vi-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3229
- Wer: 1.0
- Cer: 1.0
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 330
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:---:|:---:|
| 14.3268 | 7.5 | 165 | 3.6499 | 1.0 | 1.0 |
| 4.1213 | 15.0 | 330 | 3.3787 | 1.0 | 1.0 |
| 3.6333 | 22.5 | 495 | 3.3401 | 1.0 | 1.0 |
| 3.4432 | 30.0 | 660 | 3.3229 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
rafatsiddiqui/Meta-Llama-3-8B-SST-FineTune-Tokenizer | rafatsiddiqui | "2024-06-25T10:52:41Z" | 0 | 0 | transformers | [
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-24T02:10:20Z" | ---
library_name: transformers
tags:
- unsloth
---
# 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]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
AkhilTolani/musicgen-mmd-v2 | AkhilTolani | "2024-06-24T02:17:24Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T02:12:09Z" | Entry not found |
bk1024/test_train | bk1024 | "2024-06-25T08:22:01Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-06-24T02:22:36Z" | Entry not found |
woweenie/v72-curated2-5e6-bs6ga12-3k-main-46k-half | woweenie | "2024-06-24T02:28:01Z" | 0 | 0 | diffusers | [
"diffusers",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-06-24T02:25:24Z" | Entry not found |
schuler/hf-tiny-tokenizer-22k | schuler | "2024-06-24T02:25:43Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-24T02:25:43Z" | ---
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] |
joaopaulopresa/unsloth-Qwen2-7B-pt-instruct | joaopaulopresa | "2024-06-28T21:29:19Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-24T02:25:52Z" | ---
base_model: unsloth/Qwen2-7B
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
---
# Uploaded model
- **Developed by:** joaopaulopresa
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2-7B
This qwen2 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)
|
PrunaAI/allenai-tulu-2-7b-AWQ-4bit-smashed | PrunaAI | "2024-06-24T02:27:56Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pruna-ai",
"conversational",
"base_model:allenai/tulu-2-7b",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | "2024-06-24T02:26:12Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: allenai/tulu-2-7b
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 awq.
- ***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 allenai/tulu-2-7b installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install autoawq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from awq import AutoAWQForCausalLM
model = AutoAWQForCausalLM.from_quantized("PrunaAI/allenai-tulu-2-7b-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("allenai/tulu-2-7b")
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 allenai/tulu-2-7b 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/beomi-Yi-Ko-6B-AWQ-4bit-smashed | PrunaAI | "2024-06-24T02:30:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pruna-ai",
"base_model:beomi/Yi-Ko-6B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | "2024-06-24T02:28:52Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: beomi/Yi-Ko-6B
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 awq.
- ***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 beomi/Yi-Ko-6B installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install autoawq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from awq import AutoAWQForCausalLM
model = AutoAWQForCausalLM.from_quantized("PrunaAI/beomi-Yi-Ko-6B-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("beomi/Yi-Ko-6B")
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 beomi/Yi-Ko-6B 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). |
Vyshu2103/flan-t5-base-imdb-text-classification | Vyshu2103 | "2024-06-24T17:18:20Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | "2024-06-24T02:34:07Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: flan-t5-base-imdb-text-classification
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. -->
# flan-t5-base-imdb-text-classification
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0767
- F1: 95.084
- Gen Len: 2.4976
## 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: 0.0003
- 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: 2
### Training results
### Framework versions
- Transformers 4.28.1
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.13.3
|
japlic/monty | japlic | "2024-06-24T02:36:07Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T02:36:07Z" | Entry not found |
akhilapitla/allizzwell | akhilapitla | "2024-06-24T02:37:15Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T02:37:15Z" | Entry not found |
PrunaAI/sambanovasystems-SambaLingo-Arabic-Base-AWQ-4bit-smashed | PrunaAI | "2024-06-24T02:41:56Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pruna-ai",
"base_model:sambanovasystems/SambaLingo-Arabic-Base",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | "2024-06-24T02:39:57Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: sambanovasystems/SambaLingo-Arabic-Base
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 awq.
- ***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 sambanovasystems/SambaLingo-Arabic-Base installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install autoawq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from awq import AutoAWQForCausalLM
model = AutoAWQForCausalLM.from_quantized("PrunaAI/sambanovasystems-SambaLingo-Arabic-Base-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Arabic-Base")
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 sambanovasystems/SambaLingo-Arabic-Base 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). |
lcmoore/tutorial_model | lcmoore | "2024-06-24T03:03:01Z" | 0 | 0 | null | [
"tensorboard",
"region:us"
] | null | "2024-06-24T02:44:24Z" | Entry not found |
davidyu2023/Qwen-Qwen1.5-7B-1719197122 | davidyu2023 | "2024-06-24T02:45:33Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"region:us"
] | null | "2024-06-24T02:45:22Z" | ---
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]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## Model Card Contact
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### Framework versions
- PEFT 0.11.1 |
davidyu2023/google-gemma-2b-1719197216 | davidyu2023 | "2024-06-24T02:47:36Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | "2024-06-24T02:46:57Z" | ---
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
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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]
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & 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]
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- **Carbon Emitted:** [More Information Needed]
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### Framework versions
- PEFT 0.11.1 |
PrunaAI/galatolo-cerbero-7b-AWQ-4bit-smashed | PrunaAI | "2024-06-24T02:54:04Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"pruna-ai",
"conversational",
"base_model:galatolo/cerbero-7b",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | "2024-06-24T02:52:09Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: galatolo/cerbero-7b
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 awq.
- ***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 galatolo/cerbero-7b installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install autoawq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from awq import AutoAWQForCausalLM
model = AutoAWQForCausalLM.from_quantized("PrunaAI/galatolo-cerbero-7b-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("galatolo/cerbero-7b")
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 galatolo/cerbero-7b 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). |
nttwt1597/test_v2_cancer_v4_checkpoint2900 | nttwt1597 | "2024-06-24T02:58:09Z" | 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 | "2024-06-24T02:56:51Z" | ---
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:** nttwt1597
- **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)
|
yraziel/ebz_yr | yraziel | "2024-06-24T02:58:54Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T02:58:24Z" | Entry not found |
kevinwang676/RVC-models | kevinwang676 | "2024-06-24T12:06:35Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-24T02:58:48Z" | ---
license: mit
---
|
Razer112/Unknown | Razer112 | "2024-06-24T16:16:21Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-24T02:58:50Z" | ---
license: openrail
---
|
joaopaulopresa/unsloth-Qwen2-7B-pt-instruct2 | joaopaulopresa | "2024-06-24T22:49:17Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-24T03:01:03Z" | ---
base_model: unsloth/Qwen2-7B
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
---
# Uploaded model
- **Developed by:** joaopaulopresa
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2-7B
This qwen2 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)
|
cxfajar197/iqbaltrocr | cxfajar197 | "2024-06-24T03:03:28Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T03:03:28Z" | Entry not found |
Prakash21/prkavtar | Prakash21 | "2024-06-24T03:03:28Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-24T03:03:28Z" | ---
license: apache-2.0
---
|
davidyu2023/Qwen-Qwen1.5-0.5B-1719198696 | davidyu2023 | "2024-06-24T03:11:45Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-06-24T03:11:37Z" | ---
base_model: Qwen/Qwen1.5-0.5B
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]
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- **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 |
tsavage68/Summary_L3_50steps_1e6rate_05beta_CSFTDPO | tsavage68 | "2024-06-24T03:18:13Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:tsavage68/Summary_L3_1000steps_1e7rate_SFT2",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-24T03:11:50Z" | ---
license: llama3
base_model: tsavage68/Summary_L3_1000steps_1e7rate_SFT2
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: Summary_L3_50steps_1e6rate_05beta_CSFTDPO
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. -->
# Summary_L3_50steps_1e6rate_05beta_CSFTDPO
This model is a fine-tuned version of [tsavage68/Summary_L3_1000steps_1e7rate_SFT2](https://huggingface.co/tsavage68/Summary_L3_1000steps_1e7rate_SFT2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5962
- Rewards/chosen: 0.0976
- Rewards/rejected: -1.3577
- Rewards/accuracies: 0.1400
- Rewards/margins: 1.4553
- Logps/rejected: -17.9791
- Logps/chosen: -9.1876
- Logits/rejected: -1.0985
- Logits/chosen: -1.1002
## 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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.555 | 0.2004 | 50 | 0.5962 | 0.0976 | -1.3577 | 0.1400 | 1.4553 | -17.9791 | -9.1876 | -1.0985 | -1.1002 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.0+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1
|
davidyu2023/Qwen-Qwen1.5-1.8B-1719198814 | davidyu2023 | "2024-06-24T03:13:40Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-06-24T03:13:34Z" | ---
base_model: Qwen/Qwen1.5-1.8B
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
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### Framework versions
- PEFT 0.11.1 |
davidyu2023/google-gemma-2b-1719198905 | davidyu2023 | "2024-06-24T03:15:34Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | "2024-06-24T03:15:05Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
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## Model Details
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### Framework versions
- PEFT 0.11.1 |
underactuated/mistral_sft | underactuated | "2024-06-24T18:23:19Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-24T03:23:02Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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- **Hardware Type:** [More Information Needed]
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wwe180/099 | wwe180 | "2024-06-24T03:34:21Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-24T03:23:10Z" | ---
license: apache-2.0
---
|
kraja928/demo_mistral | kraja928 | "2024-06-25T12:41:27Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-24T03:28:13Z" | ---
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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[More Information Needed]
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DavidSilvaUB/sd-naruto-model_v2 | DavidSilvaUB | "2024-06-24T03:28:25Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T03:28:25Z" | Entry not found |
ignaciovillanueva/umt5-base-finetuned-model | ignaciovillanueva | "2024-06-24T03:30:33Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"umt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/umt5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-06-24T03:28:34Z" | ---
license: apache-2.0
base_model: google/umt5-base
tags:
- generated_from_trainer
model-index:
- name: umt5-base-finetuned-model
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. -->
# umt5-base-finetuned-model
This model is a fine-tuned version of [google/umt5-base](https://huggingface.co/google/umt5-base) on the None 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: 12
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
hoanghnss1500441/Yolo | hoanghnss1500441 | "2024-06-24T03:30:32Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-24T03:28:44Z" | ---
license: apache-2.0
---
|
ignaciovillanueva/umt5-base-finetuned-model_test_colab | ignaciovillanueva | "2024-06-24T03:33:16Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-24T03:32:33Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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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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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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### Training Data
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[More Information Needed]
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[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).
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jacket628/model_demo01 | jacket628 | "2024-06-24T03:41:43Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T03:37:34Z" | Entry not found |
jxke/Qwen1.5-4B-chat-rkllm | jxke | "2024-06-24T05:02:40Z" | 0 | 0 | null | [
"qwen",
"Qwen1.5-4B-Chat",
"rkllm",
"rk3588",
"text-generation",
"zh",
"en",
"region:us"
] | text-generation | "2024-06-24T03:39:26Z" | ---
language:
- zh
- en
pipeline_tag: text-generation
tags:
- qwen
- Qwen1.5-4B-Chat
- rkllm
- rk3588
---
# Qwen1.5-4B-Chat-rkllm
This is a conversion from [Qwen/Qwen1.5-4B-Chat](https://huggingface.co/Qwen/Qwen1.5-4B-Chat)
to the RKLLM format for chat in Rockchip devices.
- [Qwen1.5-4B-Chat-rkllm](#qwen15-4b-chat-rkllm)
- [Support Devices](#support-devices)
- [Convert tools](#convert-tools)
- [Converted with RKLLM runtime](#converted-with-rkllm-runtime)
- [License](#license)
- [Trouble shot](#trouble-shot)
- [Reference](#reference)
## Support Devices
* RK3588/RK3588s
## Convert tools
To Converting LLMs for Rockchip's NPUs, please see the artical<sup>1,2</sup> for model details.
## Converted with RKLLM runtime
* RKLLM runtime `1.0.1`
## License
Same as the original [Qwen/Qwen1.5-4B-Chat](https://huggingface.co/Qwen/Qwen1.5-4B-Chat)
## Trouble shot
* `E RKNN: [10:48:59.683] failed to allocate handle, ret: -1, errno: 12, errstr: Cannot allocate memory`
```shell
firefly@firefly:~/Documents/rknn-llm$ rkllm ./chatglm3-6b.rkllm
rkllm init start
rkllm-runtime version: 1.0.1, rknpu driver version: 0.8.2, platform: RK3588
Warning: Your rknpu driver version is too low, please upgrade to 0.9.6.
E RKNN: [10:48:59.683] failed to allocate handle, ret: -1, errno: 12, errstr: Cannot allocate memory
can not create weight memory for domain1
E RKNN: [10:49:00.480] failed to allocate handle, ret: -1, errno: 12, errstr: Cannot allocate memory
can not create weight memory for domain2
E RKNN: [10:49:05.216] failed to convert handle(1020) to fd, ret: -1, errno: 24, errstr: Too many open files
# Solution
firefly@firefly:~/Documents/rknn-llm$ ulimit -n 102400
```
## Reference
1. [airockchip/rknn-llm](https://github.com/airockchip/rknn-llm)
1. [Pelochus/ezrknn-llm](https://github.com/Pelochus/ezrknn-llm)
2. [Qwen/Qwen1.5-4B-Chat](https://huggingface.co/Qwen/Qwen1.5-4B-Chat)
3. [跑大模型遇到问题 #62](https://github.com/airockchip/rknn-llm/issues/62)
|
Kathernie/whisper_s-tamil-r_moe | Kathernie | "2024-06-24T10:35:54Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-24T03:41:08Z" | Entry not found |
Litzy619/MIS0624TEST | Litzy619 | "2024-06-24T05:42:37Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T03:41:59Z" | Entry not found |
elliotthwang/KimLan-google-gemma-2b | elliotthwang | "2024-06-24T03:42:28Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T03:42:28Z" | Entry not found |
ikocemayy13938/robinmodel | ikocemayy13938 | "2024-06-24T04:18:31Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-24T03:42:54Z" | ---
license: openrail
---
|
Gille/BiggerWizardLM-2-7B-Extended | Gille | "2024-06-24T03:49:45Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Replete-AI/WizardLM-2-7b",
"base_model:Replete-AI/WizardLM-2-7b",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-24T03:45:00Z" | ---
base_model:
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
- Replete-AI/WizardLM-2-7b
tags:
- merge
- mergekit
- lazymergekit
- Replete-AI/WizardLM-2-7b
---
# BiggerWizardLM-2-7B-Extended
BiggerWizardLM-2-7B-Extended is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
* [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 0
- 4
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 3
- 4
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 4
- 8
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 7
- 8
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 8
- 12
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 11
- 12
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 12
- 16
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 15
- 16
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 16
- 20
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 19
- 20
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 20
- 24
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 23
- 24
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 24
- 28
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 27
- 28
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 28
- 32
- sources:
- model: Replete-AI/WizardLM-2-7b
layer_range:
- 31
- 32
parameters:
scale:
- filter: o_proj
value: 0
- filter: down_proj
value: 0
- value: 1
merge_method: passthrough
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Gille/BiggerWizardLM-2-7B-Extended"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
dikdimon/fac | dikdimon | "2024-06-24T15:35:19Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T03:45:11Z" | Entry not found |
abcdef123987/Mandalay_lora | abcdef123987 | "2024-06-24T03:46:03Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T03:46:03Z" | Entry not found |
gdataviv/pegasus-multi_news-NewsSummarization_BBC | gdataviv | "2024-06-24T04:34:20Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-06-24T03:46:06Z" | Entry not found |
usagent100/testing600-v1 | usagent100 | "2024-06-24T09:48:26Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T03:51:02Z" | Entry not found |
Tung177/ss-gemma2b-lora_adapter-batchsize128 | Tung177 | "2024-06-24T03:58:29Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-2b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-24T03:58:22Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-2b-bnb-4bit
---
# Uploaded model
- **Developed by:** Tung177
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-bnb-4bit
This gemma 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)
|
Rabbwolf/Favourite-Pictorial-Model | Rabbwolf | "2024-06-24T04:00:35Z" | 0 | 1 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-24T04:00:35Z" | ---
license: apache-2.0
---
|
Ak000/Llama-2-7b-hf-dementia | Ak000 | "2024-06-24T04:08:33Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-24T04:04:26Z" | ---
library_name: transformers
tags:
- llama-factory
---
# 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]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
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<!-- 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
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[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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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]
## 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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- 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]
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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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[More Information Needed]
## Model Card Contact
[More Information Needed] |
Katyc/llama-2-7b-miniguanaco | Katyc | "2024-06-24T04:20:19Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-24T04:09:24Z" | Entry not found |
rs545837/speecht5_jenny_500 | rs545837 | "2024-06-24T04:16:29Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"endpoints_compatible",
"region:us"
] | text-to-audio | "2024-06-24T04:10:10Z" | Entry not found |
neross/servant | neross | "2024-06-24T04:14:54Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T04:14:54Z" | Entry not found |
formsKorea/snapform | formsKorea | "2024-06-27T08:53:36Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T04:16:53Z" | Entry not found |
weidaderen/dog | weidaderen | "2024-06-24T04:17:24Z" | 0 | 0 | null | [
"license:afl-3.0",
"region:us"
] | null | "2024-06-24T04:17:24Z" | ---
license: afl-3.0
---
|
Stich666/kek | Stich666 | "2024-06-24T04:20:34Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T04:20:34Z" | Entry not found |
2xionger/dummy-model | 2xionger | "2024-06-24T04:50:04Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2024-06-24T04:21:18Z" | ---
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]
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[More Information Needed]
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<!-- 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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[More Information Needed]
## Glossary [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Wawaworker/mzskgl | Wawaworker | "2024-06-24T04:49:21Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T04:22:02Z" | Entry not found |
SicariusSicariiStuff/TTS_Lola | SicariusSicariiStuff | "2024-06-24T04:27:53Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-24T04:24:55Z" | ---
license: apache-2.0
---
|
chainup244/Qwen-Qwen1.5-7B-1719203141 | chainup244 | "2024-06-24T04:25:46Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T04:25:46Z" | Entry not found |
chainup244/google-gemma-2b-1719203263 | chainup244 | "2024-06-24T04:27:45Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T04:27:45Z" | Entry not found |
atharvadeshpande925/example-model | atharvadeshpande925 | "2024-06-24T09:41:33Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T04:30:12Z" | # Example model
This is my model card README
---
license: mit
---
|
Alanscr/Sentimiento | Alanscr | "2024-06-24T04:39:10Z" | 0 | 0 | null | [
"safetensors",
"license:mit",
"region:us"
] | null | "2024-06-24T04:37:15Z" | ---
license: mit
---
|
MagicLuke/Wav2Vec2-MyST | MagicLuke | "2024-06-26T17:42:37Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"pretraining",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | "2024-06-24T04:42:25Z" | ---
license: mit
language:
- en
---
### Model Description:
This is the wav2vec2-base model being pre-trained on the My Science Tutor (MyST 470h) dataset (from [LDC](https://catalog.ldc.upenn.edu/LDC2021S05)).
The pertaining is done by using [fairseq](https://github.com/facebookresearch/fairseq/blob/main/examples/wav2vec/README.md) (wav2vec2_base_librispeech config).
The converge checkpoint is converted from PyTorch model to Hugging Face model by using a modified version of [convertor script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py) offered by Huggingface |
ParagonLight/MeteoRA-llama3-8b | ParagonLight | "2024-06-29T09:08:43Z" | 0 | 0 | null | [
"safetensors",
"arxiv:2405.13053",
"region:us"
] | null | "2024-06-24T04:42:30Z" | # MeteoRA: Multiple-tasks Embedded LoRA for Large Language Models
This repository contains the models used in the [paper](https://arxiv.org/abs/2405.13053) "MeteoRA: Multiple-tasks Embedded LoRA for Large Language Models".
The corresponding GitHub repository is [MeteoRA](https://github.com/ParagonLight/meteor-of-lora).
## Overal performance
### General performance of MeteoRA embeded LLMs with 28 LoRA adapters
We successfully apply MeteoRA to both LlaMA2-13B and LlaMA3-8B. Each model equips 28 tasks embedded in 28 LoRA adapters, respectively.
The performance of MeteoRA is comparable to the state-of-the-art. Refer to our paper for the detailed information of evaluation settings.
<!-- Evaluation results of models based on LlaMA2-13B:
Evaluation results of models based on LlaMA3-8B:
 -->
<table>
<tr>
<td><img src="images/llama2_13b_radar_graph_v3.png" alt="LlaMA2-13B" width="300"/></td>
<td><img src="images/llama3_8b_radar_graph_v3.png" alt="LlaMA3-8B" width="300"/></td>
</tr>
</table>
MeteoRA with LlaMA2-13B MeteoRA with LlaMA3-8B
### Example of *composite-3* tasks
We highlight the statistically dominant LoRA selected by MeteoRA in token level (decoded to words). The result shows that LLM with MeteoRA could achieve timely LoRA switching on both phases of input understanding and output generation. The background color gets darker when Gating network assigns a higher weight value.
## Directory structure
- `llama3_8b_lora_b`: Contains one LoRA adapter fine-tuned with 28 tasks together in balanced-dataset mode (1,000 samples for each task).
- `llama3_8b_lora_f`: Contains one LoRA adapter fine-tuned with 28 tasks together in full-dataset mode.
- `llama3_8b_meteora`: Contains the LlaMA3-8b base model equipped with MeteoRA. Both top-1 and top-2 versions included.
- `llama3_8b_peft`: Contains 28 LoRA adapters fine-tuned for 28 tasks, respectively.
## Usage
### Preparation
0. Clone the GitHub repository [MeteoRA](https://github.com/ParagonLight/meteor-of-lora).
1. Install necessary packages:
```shell
pip install -r requirements.txt
```
2. Prepare the datasets. MeteoRA requires datasets in JSONL format. The tasks are primarily selected from the BIGBench dataset in the paper, which is in JSON format. To convert them to JSONL format, run:
```shell
cd data
python create_dataset.py --task all
```
To create a specific dataset, use:
```shell
cd data
python create_dataset.py --task <task_name>
```
3. Prepare *composite-n* tasks. Refer to our paper for the definition of *composite-n* tasks. Generate these tasks using:
```shell
python create_composite.py --n <n>
```
We prepared `n=3`, `n=5` and `n=10` few-shot dataset generating code. Before generation, please ensure that the sub-tasks to composite *composite-n* task have been included in `data/datasets`.
4. Prepare LoRA adapters and MeteoRA model checkpoints. You can train them yourself or download ours pre-trained models ([MeteoRA with LlaMA2](https://huggingface.co/ParagonLight/MeteoRA-llama2-13b) and [MeteoRA with LlaMA3](https://huggingface.co/ParagonLight/MeteoRA-llama3-8b) as base model):
```shell
python download_ckpt.py
```
5. Update file paths in `configs/config.yaml`. Example paths:
```yaml
base_model_path: 'meta-llama3/Meta-Llama-3-8B'
meteora_ckpt_path: 'ckpt/llama3_8b/llama3_8b_meteora/top_2'
adapter_dir: 'ckpt/llama3_8b/llama3_8b_peft'
```
### Evaluation
Run a benchmark with the MeteoRA model:
```shell
python eval_model.py --task <task_name> --batch_size <batch_size>
```
For example:
```shell
python eval_model.py --task composite_10 --batch_size 4
```
**Note:** For *composite-n* tasks, set a larger *temperature* value (`self.T` in `MoELoRA/layer.py`). Use `15`, `20`, and `30` for `n=3`, `n=5`, and `n=10`, respectively. For single tasks, use the default value (`self.T=1`).
To save the evaluation result:
```shell
python eval_model.py --task <task_name> --batch_size <batch_size> --save
```
For debug mode (model output and ground truth will be shown in the console):
```shell
python eval_model.py --task <task_name> --batch_size <batch_size> --debug
```
Run a benchmark with the PEFT model:
```shell
python eval_model.py --task <task_name> --batch_size <batch_size> --model <adapter_name>
```
### Training the MeteoRA Model
0. Prepare LoRA adapters and corresponding datasets in JSONL format. Ensure each LoRA adapter has a corresponding dataset. Place all LoRA adapters and datasets in their respective folders with matching subfolder names:
```
- lora_adapters
- adapter_name1
- adapter_name2
- ...
- datasets
- dataset_name1
- dataset_name2
- ...
```
1. Update file paths in `run_meteora_train_fsdp.sh`.
2. Train the MeteoRA model:
```shell
sh run_meteora_train_fsdp.sh
```
**Note:** The current version of Triton acceleration supports inference mode only. Use the following settings when training the MeteoRA model:
```shell
export MOELINEAR_USE_ACCELERATE_FWD=0
export MOELINEAR_FWD_INNER_LOOP_MODE='batch'
export MOELINEAR_ACCELERATE_FWD_BACKEND='torch'
export MOELINEAR_ACCELERATE_FWD_BACKEND_TORCH_VERSION='v1'
```
### Evaluation Results
#### 1.
#### 2. *composite-n* results
The *composite-10* evaluation results are presented in details with MeteoRA results on the left side and LoRA-B results on the right side of each metric column. A dash ('-') indicates that the corresponding metric was not applicable or included in the evaluation. Note that the `0.00` BLEU scores are caused by mismatch and too insufficient answers.
| Sub-task Name | Accuracy↑ (MeteoRA) | Accuracy↑ (LoRA-B) | BLEU↑ (MeteoRA) | BLEU↑ (LoRA-B) | ROUGE-1↑ (MeteoRA) | ROUGE-1↑ (LoRA-B) | ROUGE-2↑ (MeteoRA) | ROUGE-2↑ (LoRA-B) | ROUGE-L↑ (MeteoRA) | ROUGE-L↑ (LoRA-B) |
|--------------------------------|---------------------|--------------------|-----------------|----------------|---------------------|--------------------|---------------------|--------------------|---------------------|--------------------|
| logical_deduction | 0.500↑ | 0.453 | - | - | - | - | - | - | - | - |
| question_selection | 0.703↑ | 0.688 | - | - | - | - | - | - | - | - |
| abstract_narrative_understanding| 0.625↓ | 0.672 | - | - | - | - | - | - | - | - |
| goal_step_wikihow | 0.773↑ | 0.727 | - | - | - | - | - | - | - | - |
| winowhy | 0.422↑ | 0.078 | - | - | - | - | - | - | - | - |
| strategyqa | 0.461↑ | 0.211 | 3.23↑ | 0.00 | 0.225↑ | 0.106 | 0.051↑ | 0.025 | 0.210↑ | 0.099 |
| disfl_qa | 0.266↑ | 0.117 | - | - | - | - | - | - | - | - |
| news_commentary_de | - | - | 14.78↑ | 14.54 | - | - | - | - | - | - |
| alpaca | - | - | 0.00↓ | 8.17 | 0.257↑ | 0.187 | 0.075 | 0.075 | 0.241↑ | 0.167 |
| linguistics_puzzles | - | - | 17.37↑ | 12.14 | 0.233↑ | 0.189 | 0.052↑ | 0.030 | 0.176↑ | 0.103 |
## Citation
If you use MeteoRA for your research, please cite our [paper](https://arxiv.org/abs/2405.13053):
```bibtex
@misc{xu2024meteora,
title={MeteoRA: Multiple-tasks Embedded LoRA for Large Language Models},
author={Jingwei Xu and Junyu Lai and Yunpeng Huang},
year={2024},
eprint={2405.13053},
archivePrefix={arXiv},
}
``` |
mytm20126/mistral-merged-kdd-v2-temp1 | mytm20126 | "2024-06-24T04:43:08Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T04:43:08Z" | Entry not found |
chainup244/Qwen-Qwen1.5-7B-1719204339 | chainup244 | "2024-06-24T04:45:44Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T04:45:44Z" | Entry not found |
bigstorm/Yi-1.5-34B-Chat-16K-8.0bpw-8hb-exl2 | bigstorm | "2024-06-24T04:57:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2403.04652",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"exl2",
"region:us"
] | text-generation | "2024-06-24T04:47:47Z" | ---
license: apache-2.0
---
# BigStorm - ExLLamaV2 (Exl2) Quantization
- 8.0 bpw target
- 8 head bits
Enjoy! Raise an issue if you'd like other BPW levels.
**Base Model Card Follows:**
---
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://01-ai.github.io/">💪 Tech Blog</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K, 16K, 32K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI)|
| Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
| Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
| Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
| Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
| Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
| Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
| Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
| Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🟣 wisemodel](https://wisemodel.cn/organization/01.AI) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
Yi-1.5-9B is the top performer among similarly sized open-source models.
# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
|
lithiumice/syncnet | lithiumice | "2024-06-24T04:53:44Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T04:48:02Z" | Entry not found |
Anh-Chan/yolov8l | Anh-Chan | "2024-06-24T04:50:37Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-24T04:50:03Z" | ---
license: mit
---
|
gwong001/hugging | gwong001 | "2024-06-24T07:36:26Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | "2024-06-24T04:51:02Z" | Entry not found |
sudhanshu746/Deepseek-math-7B-mk-4bit | sudhanshu746 | "2024-06-24T04:59:33Z" | 0 | 0 | transformers | [
"transformers",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | text-generation | "2024-06-24T04:57:29Z" | Entry not found |
ganang/llama2 | ganang | "2024-06-24T05:04:10Z" | 0 | 0 | null | [
"license:llama2",
"region:us"
] | null | "2024-06-24T05:04:10Z" | ---
license: llama2
---
|
blacksnow666/Malaysian-llama-3-8b-instruct-16k-bonito-v1 | blacksnow666 | "2024-06-24T05:08:26Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"BatsResearch/bonito-v1",
"mesolitica/malaysian-llama-3-8b-instruct-16k-post",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-24T05:04:55Z" | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- BatsResearch/bonito-v1
- mesolitica/malaysian-llama-3-8b-instruct-16k-post
---
# Malaysian-llama-3-8b-instruct-16k-bonito-v1
Malaysian-llama-3-8b-instruct-16k-bonito-v1 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [BatsResearch/bonito-v1](https://huggingface.co/BatsResearch/bonito-v1)
* [mesolitica/malaysian-llama-3-8b-instruct-16k-post](https://huggingface.co/mesolitica/malaysian-llama-3-8b-instruct-16k-post)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: BatsResearch/bonito-v1
layer_range: [0, 32]
- model: mesolitica/malaysian-llama-3-8b-instruct-16k-post
layer_range: [0, 32]
merge_method: slerp
base_model: BatsResearch/bonito-v1
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
``` |
scfengv/TVL_ASR | scfengv | "2024-06-24T05:05:29Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T05:05:29Z" | Entry not found |
sionic-ai/korean-boosted | sionic-ai | "2024-06-24T06:54:22Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T05:05:58Z" | Entry not found |
yuekai/icefall_asr_multi-hans_whisper_qwen2_7B | yuekai | "2024-06-26T01:26:07Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-24T05:07:53Z" | Entry not found |