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# Uploaded model
- **Developed by:** merkle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | merkle/lora_model_yahma | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T22:19:55+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/ufzpqjg | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
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"arxiv:1910.09700",
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| null | 2024-04-27T22:20:16+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/8l6r6gi | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
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| null | 2024-04-27T22:20:21+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/a051m0o | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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| null | 2024-04-27T22:20:26+00:00 |
null | peft |
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### Framework versions
- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "meta-llama/Meta-Llama-3-8B"} | yiyic/llama-text-labels-lora-clf | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B",
"region:us"
]
| null | 2024-04-27T22:20:27+00:00 |
null | null | {} | konradopl/gehshit | null | [
"region:us"
]
| null | 2024-04-27T22:20:47+00:00 |
|
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | GamblerOnTrain/IPZZ002 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T22:20:57+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | GamblerOnTrain/SONE021 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T22:20:58+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## 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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### Recommendations
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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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | GamblerOnTrain/JUQ323 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T22:24:46+00:00 |
text-generation | transformers | {"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"]} | ambrosfitz/phi-history | null | [
"transformers",
"tensorboard",
"safetensors",
"phi3",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"custom_code",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T22:25:08+00:00 |
|
text-generation | transformers | {} | mfaizanakhtar/Llama-2-python-Faizan-ModelC | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T22:26:06+00:00 |
|
text2text-generation | transformers | {} | alexandro767/my_t5_base_for_hw2_desc | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T22:28:18+00:00 |
|
text-generation | null |
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-70B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
See the snippet below for usage with Transformers:
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-70B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="cuda",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3).
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-70B-Instruct --include "original/*" --local-dir Meta-Llama-3-70B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
| {"language": ["en"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity\u2019s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama 3\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means, collectively, Meta\u2019s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta\u2019s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display \u201cBuilt with Meta Llama 3\u201d on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include \u201cLlama 3\u201d at the beginning of any such AI model name.\nii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a \u201cNotice\u201d text file distributed as a part of such copies: \u201cMeta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved.\u201d\niv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof).\n2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee\u2019s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \u201cAS IS\u201d BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use \u201cLlama 3\u201d (the \u201cMark\u201d) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.\nb. Subject to Meta\u2019s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n#### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n 1. Violence or terrorism\n 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n 3. Human trafficking, exploitation, and sexual violence\n 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n 5. Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices\n 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials\n 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:\n 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n 2. Guns and illegal weapons (including weapon development)\n 3. Illegal drugs and regulated/controlled substances\n 4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means:\n * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).", "extra_gated_button_content": "Submit"} | pvbhanuteja/Meta-Llama-3-70B-Instruct-GGUF | null | [
"gguf",
"facebook",
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"llama",
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"text-generation",
"en",
"license:other",
"region:us"
]
| null | 2024-04-27T22:28:25+00:00 |
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Nous-Capybara-3B-V1.9 - GGUF
- Model creator: https://huggingface.co/NousResearch/
- Original model: https://huggingface.co/NousResearch/Nous-Capybara-3B-V1.9/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Nous-Capybara-3B-V1.9.Q2_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q2_K.gguf) | Q2_K | 1.01GB |
| [Nous-Capybara-3B-V1.9.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.IQ3_XS.gguf) | IQ3_XS | 1.11GB |
| [Nous-Capybara-3B-V1.9.IQ3_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.IQ3_S.gguf) | IQ3_S | 1.17GB |
| [Nous-Capybara-3B-V1.9.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q3_K_S.gguf) | Q3_K_S | 1.17GB |
| [Nous-Capybara-3B-V1.9.IQ3_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.IQ3_M.gguf) | IQ3_M | 1.23GB |
| [Nous-Capybara-3B-V1.9.Q3_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q3_K.gguf) | Q3_K | 1.3GB |
| [Nous-Capybara-3B-V1.9.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q3_K_M.gguf) | Q3_K_M | 1.3GB |
| [Nous-Capybara-3B-V1.9.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q3_K_L.gguf) | Q3_K_L | 1.4GB |
| [Nous-Capybara-3B-V1.9.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.IQ4_XS.gguf) | IQ4_XS | 1.43GB |
| [Nous-Capybara-3B-V1.9.Q4_0.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q4_0.gguf) | Q4_0 | 1.5GB |
| [Nous-Capybara-3B-V1.9.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.IQ4_NL.gguf) | IQ4_NL | 1.51GB |
| [Nous-Capybara-3B-V1.9.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q4_K_S.gguf) | Q4_K_S | 1.51GB |
| [Nous-Capybara-3B-V1.9.Q4_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q4_K.gguf) | Q4_K | 1.59GB |
| [Nous-Capybara-3B-V1.9.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q4_K_M.gguf) | Q4_K_M | 1.59GB |
| [Nous-Capybara-3B-V1.9.Q4_1.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q4_1.gguf) | Q4_1 | 1.65GB |
| [Nous-Capybara-3B-V1.9.Q5_0.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q5_0.gguf) | Q5_0 | 1.81GB |
| [Nous-Capybara-3B-V1.9.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q5_K_S.gguf) | Q5_K_S | 1.81GB |
| [Nous-Capybara-3B-V1.9.Q5_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q5_K.gguf) | Q5_K | 1.86GB |
| [Nous-Capybara-3B-V1.9.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q5_K_M.gguf) | Q5_K_M | 1.86GB |
| [Nous-Capybara-3B-V1.9.Q5_1.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q5_1.gguf) | Q5_1 | 1.96GB |
| [Nous-Capybara-3B-V1.9.Q6_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf/blob/main/Nous-Capybara-3B-V1.9.Q6_K.gguf) | Q6_K | 2.14GB |
Original model description:
---
language:
- eng
tags:
- sft
- StableLM
license:
- mit
datasets:
- LDJnr/Capybara
- LDJnr/LessWrong-Amplify-Instruct
- LDJnr/Pure-Dove
- LDJnr/Verified-Camel
---
## **Nous-Capybara-3B V1.9**
The Capybara series is the first Nous collection of dataset and models made by fine-tuning mostly on data created by Nous in-house.
We leverage our novel data synthesis technique called Amplify-instruct (Paper coming soon), the seed distribution and synthesis method are comprised of a synergistic combination of top performing existing data synthesis techniques and distributions used for SOTA models such as Airoboros, Evol-Instruct(WizardLM), Orca, Vicuna, Know_Logic, Lamini, FLASK and others, all into one lean holistically formed methodology for the dataset and model. The seed instructions used for the start of synthesized conversations are largely based on highly datasets like Airoboros, Know logic, EverythingLM, GPTeacher and even entirely new seed instructions derived from posts on the website LessWrong, as well as being supplemented with certain in-house multi-turn datasets like Dove(A successor to Puffin).
While performing great in it's current state, the current dataset used for fine-tuning is entirely contained within 20K training examples, this is 10 times smaller than many similar performing current models, this is signficant when it comes to scaling implications for our next generation of models once we scale our novel syntheiss methods to significantly more examples.
## Process of creation and special thank yous!
This model was fine-tuned by Nous Research as part of the Capybara/Amplify-Instruct project led by Luigi D.(LDJ) (Paper coming soon), as well as significant dataset formation contributions by J-Supha and general compute and experimentation management by Jeffrey Q. during ablations.
Special thank you to **A16Z** for sponsoring our training, as well as **Yield Protocol** for their support in financially sponsoring resources during the R&D of this project.
## Thank you to those of you that have indirectly contributed!
While most of the tokens within Capybara are newly synthsized and part of datasets like Puffin/Dove, we would like to credit the single-turn datasets we leveraged as seeds that are used to generate the multi-turn data as part of the Amplify-Instruct synthesis.
The datasets shown in green below are datasets that we sampled from to curate seeds that are used during Amplify-Instruct synthesis for this project.
Datasets in Blue are in-house curations that previously existed prior to Capybara.
## Model Training
Nous-Capybara 3B V1.9 is a new model trained for multiple epochs on a dataset of roughly 20,000 carefully curated conversational examples, most of which are comprised of entirely new in-house synthesized tokens.
Additional data came from human curated CamelAI data, with the help of volunteers ranging from former Physics PhD's, Mathematicians, Biologists and more!
## Prompt Format
The model follows ChatML prompt format
```
<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
How are you<|im_end|>
<|im_start|>assistant
I am doing well!<|im_end|>
```
## Mutli-Modality!
- We currently have a Multi-modal model based on Capybara-3B-V1.9 !
https://huggingface.co/NousResearch/Obsidian-3B-V0.5
## Notable Features:
- Over 60% of the dataset is comprised of multi-turn conversations.(Most models are still only trained for single-turn conversations and no back and forths!)
- Over 1,000 tokens average per conversation example! (Most models are trained on conversation data that is less than 300 tokens per example.)
- Able to effectively do complex summaries of advanced topics and studies. (trained on hundreds of advanced difficult summary tasks developed in-house)
- Ability to recall information upto late 2022 without internet.
- Includes a portion of conversational data synthesized from less wrong posts, discussing very in-depth details and philosophies about the nature of reality, reasoning, rationality, self-improvement and related concepts.
## Example Outputs!:
## Benchmarks! (Coming soon!)
## Future Changes
This is a relatively early build amongst the grand plans for the future of Capybara!
## Future model sizes
Capybara V1.9 now currently has a 3B ad 7B size, and we plan to eventually have a 13B and 70B version in the future, as well as a potential 1B version based on phi-1.5 or Tiny Llama.
## How you can help!
In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from our training curations.
If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord!
## Dataset contamination.
We have checked the capybara dataset for contamination for several of the most popular datasets and can confirm that there is no contaminaton found.
We leveraged minhash to check for 100%, 99%, 98% and 97% similarity matches between our data and the questions and answers in benchmarks, we found no exact matches, nor did we find any matches down to the 97% similarity level.
The following are benchmarks we checked for contamination against our dataset:
- HumanEval
- AGIEval
- TruthfulQA
- MMLU
- GPT4All
| {} | RichardErkhov/NousResearch_-_Nous-Capybara-3B-V1.9-gguf | null | [
"gguf",
"region:us"
]
| null | 2024-04-27T22:30:08+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## 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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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/j6tdy0b | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T22:30:10+00:00 |
text-generation | transformers |
# stablelm-2-zephyr-1.6b-slerp11
stablelm-2-zephyr-1.6b-slerp11 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [aipib/stablelm-2-zephyr-1.6b-slerpx10](https://huggingface.co/aipib/stablelm-2-zephyr-1.6b-slerpx10)
* [stabilityai/stablelm-2-zephyr-1_6b](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: aipib/stablelm-2-zephyr-1.6b-slerpx10
layer_range: [0, 24]
- model: stabilityai/stablelm-2-zephyr-1_6b
layer_range: [0, 24]
merge_method: slerp
base_model: aipib/stablelm-2-zephyr-1.6b-slerpx10
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
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "aipib/stablelm-2-zephyr-1.6b-slerp11"
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"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "aipib/stablelm-2-zephyr-1.6b-slerpx10", "stabilityai/stablelm-2-zephyr-1_6b"], "base_model": ["aipib/stablelm-2-zephyr-1.6b-slerpx10", "stabilityai/stablelm-2-zephyr-1_6b"]} | aipib/stablelm-2-zephyr-1.6b-slerp11 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"aipib/stablelm-2-zephyr-1.6b-slerpx10",
"stabilityai/stablelm-2-zephyr-1_6b",
"conversational",
"base_model:aipib/stablelm-2-zephyr-1.6b-slerpx10",
"base_model:stabilityai/stablelm-2-zephyr-1_6b",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T22:31:44+00:00 |
null | peft |
# Model Card for Model ID
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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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### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "shrenikb/sparsegpt50sparsitymodel"} | shrenikb/sparsegpt0sparsityiidsplit | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:shrenikb/sparsegpt50sparsitymodel",
"region:us"
]
| null | 2024-04-27T22:34:43+00:00 |
null | peft |
# Model Card for Model ID
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## Model Details
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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.
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<!-- 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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[More Information Needed]
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[More Information Needed]
### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "shrenikb/sparsegpt50sparsitymodel"} | shrenikb/sparsegpt0sparsityiidsplitagg | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:shrenikb/sparsegpt50sparsitymodel",
"region:us"
]
| null | 2024-04-27T22:34:53+00:00 |
null | null | {} | GraydientPlatformAPI/loras-april28 | null | [
"region:us"
]
| null | 2024-04-27T22:37:01+00:00 |
|
null | null | {} | sophiex/config_name_xxxxx | null | [
"region:us"
]
| null | 2024-04-27T22:37:28+00:00 |
|
text-classification | transformers |
# Model Card for Model ID
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## Model Details
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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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[More Information Needed] | {"library_name": "transformers", "tags": []} | donutsan/BigBirdV1 | null | [
"transformers",
"safetensors",
"big_bird",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T22:38:17+00:00 |
text-generation | transformers |
<!-- 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. -->
# 0.1_4iters_bs256_nodpo_only4w_iter_4
This model is a fine-tuned version of [ShenaoZhang/0.1_4iters_bs256_nodpo_only4w_iter_3](https://huggingface.co/ShenaoZhang/0.1_4iters_bs256_nodpo_only4w_iter_3) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZhang/0.1_4iters_bs256_nodpo_only4w_iter_3", "model-index": [{"name": "0.1_4iters_bs256_nodpo_only4w_iter_4", "results": []}]} | ShenaoZhang/0.1_4iters_bs256_nodpo_only4w_iter_4 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.1_4iters_bs256_nodpo_only4w_iter_3",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T22:39:25+00:00 |
null | null | {"license": "openrail"} | Frixi/Sommerset | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T22:40:35+00:00 |
|
text-generation | transformers |
# 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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### 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. -->
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Yasusan/test_10B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T22:41:11+00:00 |
text-generation | transformers | {} | Tristan/pythia-410m-deduped-en-no-peft | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T22:44:29+00:00 |
|
null | null |
<!-- 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. -->
# gm-lora-bfloat16-idefics2-8b-docvqa-finetuned-tutorial
This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.8915
- eval_runtime: 91.835
- eval_samples_per_second: 2.178
- eval_steps_per_second: 1.089
- epoch: 2.0
- step: 100
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "HuggingFaceM4/idefics2-8b", "model-index": [{"name": "gm-lora-bfloat16-idefics2-8b-docvqa-finetuned-tutorial", "results": []}]} | gimarchetti/gm-lora-bfloat16-idefics2-8b-docvqa-finetuned-tutorial | null | [
"safetensors",
"generated_from_trainer",
"base_model:HuggingFaceM4/idefics2-8b",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T22:48:07+00:00 |
text-classification | transformers |
# 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. -->
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### Downstream Use [optional]
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[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] | {"library_name": "transformers", "tags": []} | wingsteels/layoutlmv3-uroculture-classification | null | [
"transformers",
"safetensors",
"layoutlmv3",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T22:48:17+00:00 |
text-generation | transformers |
# Llama 3 70B Instruct – OmniQuant
Based on [Llama 3 70B Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-70B-Instruct).
Quantized with [OmniQuant](https://github.com/OpenGVLab/OmniQuant).
## Evaluation
### PPL (↓)
| | wiki |
| ------------- | ---- |
| FP | 5,33 |
| **Quantized** | 5,90 |
### Accuracy on English Benchmarks, % (↑)
| | piqa | arc_easy | arc_challenge | boolq | hellaswag | winogrande | mmlu_humanities | mmlu_social_sciences | mmlu_stem | mmlu_other |
| ------------- | ---- | -------- | ------------- | ----- | --------- | ---------- | --------------- | -------------------- | --------- | ---------- |
| FP | 81,5 | 86,2 | 61,9 | 87,4 | 63,7 | 75,8 | 78,7 | 84,4 | 71,1 | 80,2 |
| **Quantized** | 80,7 | 85,8 | 61,4 | 87,0 | 62,7 | 73,0 | 75,5 | 81,0 | 68,6 | 77,9 |
### Accuracy on Russian Benchmarks, % (↑)
| | danetqa | terra | rwsd | muserc | rucos | lidirus | parus | rcb | russe | rucola |
| ------------- | ------- | ----- | ---- | ------ | ----- | ------- | ----- | ---- | ----- | ------ |
| FP | 88,9 | 88,6 | 75,5 | 81,8 | 82,4 | 70,7 | 77,0 | 35,0 | 63,1 | 34,7 |
| **Quantized** | 86,6 | 81,8 | 71,6 | 75,6 | 69,5 | 60,3 | 64,0 | 26,8 | 63,1 | 32,5 |
### Summary
| | Avg acc diff on Eng, % (↑) | Avg acc diff on Rus, % (↑) | Occupied disk space, % (↓) |
| ------------- | -------------------------- | -------------------------- | -------------------------- |
| FP | 0 | 0 | 100 |
| **Quantized** | \-1,7 | \-6,6 | 28,2 |
## Examples
### Imports and Model Loading
<details>
<summary>Expand</summary>
```python
import gc
import auto_gptq.nn_modules.qlinear.qlinear_cuda as qlinear_cuda
import auto_gptq.nn_modules.qlinear.qlinear_triton as qlinear_triton
import torch
from accelerate import (
init_empty_weights,
infer_auto_device_map,
load_checkpoint_in_model,
)
from tqdm import tqdm
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
pipeline,
)
def get_named_linears(model):
return {
name: module for name, module in model.named_modules()
if isinstance(module, torch.nn.Linear)
}
def set_module(model, name, module):
parent = model
levels = name.split('.')
for i in range(len(levels) - 1):
cur_name = levels[i]
if cur_name.isdigit():
parent = parent[int(cur_name)]
else:
parent = getattr(parent, cur_name)
setattr(parent, levels[-1], module)
def load_model(model_path):
# Based on: https://github.com/OpenGVLab/OmniQuant/blob/main/runing_quantized_mixtral_7bx8.ipynb
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
if not hasattr(config, 'quantization_config'):
raise AttributeError(
f'No quantization info found in model config "{model_path}"'
f' (`quantization_config` section is missing).'
)
wbits = config.quantization_config['bits']
group_size = config.quantization_config['group_size']
# We are going to init an ordinary model and then manually replace all Linears with QuantLinears
del config.quantization_config
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config, torch_dtype=torch.float16, trust_remote_code=True)
layers = model.model.layers
for i in tqdm(range(len(layers))):
layer = layers[i]
named_linears = get_named_linears(layer)
for name, module in named_linears.items():
params = (
wbits, group_size,
module.in_features, module.out_features,
module.bias is not None
)
if wbits in [2, 4]:
q_linear = qlinear_triton.QuantLinear(*params)
elif wbits == 3:
q_linear = qlinear_cuda.QuantLinear(*params)
else:
raise NotImplementedError("Only 2, 3 and 4 bits are supported.")
q_linear.to(next(layer.parameters()).device)
set_module(layer, name, q_linear)
torch.cuda.empty_cache()
gc.collect()
model.tie_weights()
device_map = infer_auto_device_map(model)
print("Loading pre-computed quantized weights...")
load_checkpoint_in_model(
model, checkpoint=model_path,
device_map=device_map, offload_state_dict=True,
)
print("Model loaded successfully!")
return model
```
</details>
### Inference
```python
model_path = "compressa-ai/Llama-3-70B-Instruct-OmniQuant"
model = load_model(model_path).cuda()
tokenizer = AutoTokenizer.from_pretrained(
model_path, use_fast=False, trust_remote_code=True
)
# Llama 3 "specifics"
# https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/discussions/4
terminators = [
tokenizer.convert_tokens_to_ids("<|end_of_text|>"),
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
system_message = "You are a friendly chatbot who responds as if you are the Sandy Cheeks squirrel from the SpongeBob SquarePants cartoon."
user_message = "Do squirrels communicate with birds?"
messages = [
{"role": "system", "content": system_message},
{"role": "user", "content": user_message},
]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(prompt, return_tensors="pt")
inputs = {k: v.cuda() for k, v in inputs.items()}
outputs = model.generate(
**inputs, max_new_tokens=512,
do_sample=True, temperature=0.7, top_p=0.95,
eos_token_id=terminators,
)
response = tokenizer.decode(outputs[0])
continuation = response.removeprefix(prompt).removesuffix(tokenizer.eos_token)
print(f'Prompt:\n{prompt}')
print(f'Continuation:\n{continuation}\n')
```
### Inference Using Pipeline
```python
pipe = pipeline(
"text-generation",
model=model, tokenizer=tokenizer,
eos_token_id=terminators,
max_new_tokens=512, do_sample=True,
temperature=0.7, top_p=0.95,
device=0,
)
prompt = pipe.tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
outputs = pipe(prompt)
response = outputs[0]["generated_text"]
continuation = response.removeprefix(prompt)
print(f'Prompt:\n{prompt}')
print(f'Continuation:\n{continuation}\n')
```
| {"license": "other", "tags": ["llama3", "omniquant", "gptq", "triton"], "base_model": "NousResearch/Meta-Llama-3-70B-Instruct", "model_type": "llama", "pipeline_tag": "text-generation", "quantized_by": "Compressa", "license_name": "llama3", "license_link": "https://llama.meta.com/llama3/license"} | compressa-ai/Llama-3-70B-Instruct-OmniQuant | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama3",
"omniquant",
"gptq",
"triton",
"conversational",
"base_model:NousResearch/Meta-Llama-3-70B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-27T22:48:26+00:00 |
text-generation | transformers | {} | Tristan/pythia-410m-deduped-default-no-peft | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T22:48:52+00:00 |
|
text-generation | transformers |
# 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]
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## 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]
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## Technical Specifications [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/uwal0yd | null | [
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"arxiv:1910.09700",
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| null | 2024-04-27T22:49:09+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/xd0nx3q | null | [
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| null | 2024-04-27T22:49:14+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/sj02dkb | null | [
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"arxiv:1910.09700",
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"endpoints_compatible",
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| null | 2024-04-27T22:49:19+00:00 |
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
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Nous-Hermes-2-Yi-34B - GGUF
- Model creator: https://huggingface.co/NousResearch/
- Original model: https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Nous-Hermes-2-Yi-34B.Q2_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q2_K.gguf) | Q2_K | 11.94GB |
| [Nous-Hermes-2-Yi-34B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.IQ3_XS.gguf) | IQ3_XS | 13.26GB |
| [Nous-Hermes-2-Yi-34B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.IQ3_S.gguf) | IQ3_S | 13.99GB |
| [Nous-Hermes-2-Yi-34B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q3_K_S.gguf) | Q3_K_S | 13.93GB |
| [Nous-Hermes-2-Yi-34B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.IQ3_M.gguf) | IQ3_M | 14.5GB |
| [Nous-Hermes-2-Yi-34B.Q3_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q3_K.gguf) | Q3_K | 15.51GB |
| [Nous-Hermes-2-Yi-34B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q3_K_M.gguf) | Q3_K_M | 15.51GB |
| [Nous-Hermes-2-Yi-34B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q3_K_L.gguf) | Q3_K_L | 16.89GB |
| [Nous-Hermes-2-Yi-34B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.IQ4_XS.gguf) | IQ4_XS | 17.36GB |
| [Nous-Hermes-2-Yi-34B.Q4_0.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q4_0.gguf) | Q4_0 | 18.13GB |
| [Nous-Hermes-2-Yi-34B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.IQ4_NL.gguf) | IQ4_NL | 18.3GB |
| [Nous-Hermes-2-Yi-34B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q4_K_S.gguf) | Q4_K_S | 18.25GB |
| [Nous-Hermes-2-Yi-34B.Q4_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q4_K.gguf) | Q4_K | 19.24GB |
| [Nous-Hermes-2-Yi-34B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q4_K_M.gguf) | Q4_K_M | 19.24GB |
| [Nous-Hermes-2-Yi-34B.Q4_1.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q4_1.gguf) | Q4_1 | 20.1GB |
| [Nous-Hermes-2-Yi-34B.Q5_0.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q5_0.gguf) | Q5_0 | 22.08GB |
| [Nous-Hermes-2-Yi-34B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q5_K_S.gguf) | Q5_K_S | 22.08GB |
| [Nous-Hermes-2-Yi-34B.Q5_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q5_K.gguf) | Q5_K | 22.65GB |
| [Nous-Hermes-2-Yi-34B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q5_K_M.gguf) | Q5_K_M | 22.65GB |
| [Nous-Hermes-2-Yi-34B.Q5_1.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q5_1.gguf) | Q5_1 | 24.05GB |
| [Nous-Hermes-2-Yi-34B.Q6_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf/blob/main/Nous-Hermes-2-Yi-34B.Q6_K.gguf) | Q6_K | 26.28GB |
Original model description:
---
base_model: 01-ai/Yi-34B
tags:
- yi
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
model-index:
- name: Nous-Hermes-2-Yi-34B
results: []
license: apache-2.0
language:
- en
datasets:
- teknium/OpenHermes-2.5
---
# Nous Hermes 2 - Yi-34B
## Model description
Nous Hermes 2 - Yi-34B is a state of the art Yi Fine-tune.
Nous Hermes 2 Yi 34B was trained on 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape.
# Table of Contents
1. [Example Outputs](#example-outputs)
- Discussing the Laws of Gravity
- Create a Flask based FTP Server
2. [Benchmark Results](#benchmark-results)
- GPT4All
- AGIEval
- BigBench
- Averages Compared
3. [Prompt Format](#prompt-format)
4. [Quantized Models](#quantized-models)
## Example Outputs
### Discussions about the Law of Gravity:
### Create an FTP Server in FLASK:
## Benchmark Results
Nous-Hermes 2 on Yi 34B outperforms all Nous-Hermes & Open-Hermes models of the past, achieving new heights in all benchmarks for a Nous Research LLM as well as surpassing many popular finetunes.
# Benchmarks Compared
### GPT4All:
### AGIEval:
### BigBench:
### TruthfulQA:
## GPT4All
GPT-4All Benchmark Set
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.6067|_ |0.0143|
| | |acc_norm|0.6416|_ |0.0140|
|arc_easy | 0|acc |0.8594|_ |0.0071|
| | |acc_norm|0.8569|_ |0.0072|
|boolq | 1|acc |0.8859|_ |0.0056|
|hellaswag | 0|acc |0.6407|_ |0.0048|
| | |acc_norm|0.8388|_ |0.0037|
|openbookqa | 0|acc |0.3520|_ |0.0214|
| | |acc_norm|0.4760|_ |0.0224|
|piqa | 0|acc |0.8215|_ |0.0089|
| | |acc_norm|0.8303|_ |0.0088|
|winogrande | 0|acc |0.7908|_ |0.0114|
Average: 76.00%
```
AGI-Eval
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.3189|_ |0.0293|
| | |acc_norm|0.2953|_ |0.0287|
|agieval_logiqa_en | 0|acc |0.5438|_ |0.0195|
| | |acc_norm|0.4977|_ |0.0196|
|agieval_lsat_ar | 0|acc |0.2696|_ |0.0293|
| | |acc_norm|0.2087|_ |0.0269|
|agieval_lsat_lr | 0|acc |0.7078|_ |0.0202|
| | |acc_norm|0.6255|_ |0.0215|
|agieval_lsat_rc | 0|acc |0.7807|_ |0.0253|
| | |acc_norm|0.7063|_ |0.0278|
|agieval_sat_en | 0|acc |0.8689|_ |0.0236|
| | |acc_norm|0.8447|_ |0.0253|
|agieval_sat_en_without_passage| 0|acc |0.5194|_ |0.0349|
| | |acc_norm|0.4612|_ |0.0348|
|agieval_sat_math | 0|acc |0.4409|_ |0.0336|
| | |acc_norm|0.3818|_ |0.0328|
Average: 50.27%
```
BigBench Reasoning Test
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.5737|_ |0.0360|
|bigbench_date_understanding | 0|multiple_choice_grade|0.7263|_ |0.0232|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3953|_ |0.0305|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.4457|_ |0.0263|
| | |exact_str_match |0.0000|_ |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2820|_ |0.0201|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2186|_ |0.0156|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4733|_ |0.0289|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.5200|_ |0.0224|
|bigbench_navigate | 0|multiple_choice_grade|0.4910|_ |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.7495|_ |0.0097|
|bigbench_ruin_names | 0|multiple_choice_grade|0.5938|_ |0.0232|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.3808|_ |0.0154|
|bigbench_snarks | 0|multiple_choice_grade|0.8066|_ |0.0294|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.5101|_ |0.0159|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.3850|_ |0.0154|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2160|_ |0.0116|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1634|_ |0.0088|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4733|_ |0.0289|
Average: 46.69%
```
TruthfulQA:
```
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.4333|_ |0.0173|
| | |mc2 |0.6034|_ |0.0149|
```
Average Score Comparison between OpenHermes-1 Llama-2 13B and OpenHermes-2 Mistral 7B against OpenHermes-2.5 on Mistral-7B:
```
| Bench | OpenHermes-2.5 Mistral 7B | Nous-Hermes-2-Yi-34B | Change/OpenHermes2 |
|---------------|---------------------------|----------------------|--------------------|
|GPT4All | 73.12| 76.00| +2.88|
|---------------------------------------------------------------------------------------|
|BigBench | 40.96| 46.69| +5.73|
|---------------------------------------------------------------------------------------|
|AGI Eval | 43.07| 50.27| +7.20|
|---------------------------------------------------------------------------------------|
|TruthfulQA | 53.04| 60.34| +7.30|
|---------------------------------------------------------------------------------------|
|Total Score | 210.19| 233.30| +23.11|
|---------------------------------------------------------------------------------------|
|Average Total | 52.38| 58.33| +5.95|
```
# Prompt Format
Nous Hermes 2 uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
```
This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
When quantized versions of the model are released, I recommend using LM Studio for chatting with Nous Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
In LM-Studio, simply select the ChatML Prefix on the settings side pane:
# Quantized Models:
GGUF: https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B-GGUF
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
| {} | RichardErkhov/NousResearch_-_Nous-Hermes-2-Yi-34B-gguf | null | [
"gguf",
"region:us"
]
| null | 2024-04-27T22:51:27+00:00 |
null | null | {"license": "openrail"} | TheAmir/Pandakungfukar | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T22:51:41+00:00 |
|
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_total_Instruction0_OAPSL_v1_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_total_Instruction0_OAPSL_v1_h1", "results": []}]} | ThuyNT/CS505_COQE_viT5_total_Instruction0_OAPSL_v1_h1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T22:52:21+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_total_Instruction0_AOPSL_v1_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_total_Instruction0_AOPSL_v1_h1", "results": []}]} | ThuyNT/CS505_COQE_viT5_total_Instruction0_AOPSL_v1_h1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T22:52:31+00:00 |
null | null |
# Kaoeiri/Keiana-L3-Test5.75-8B-13.5-Q6_K-GGUF
This model was converted to GGUF format from [`Kaoeiri/Keiana-L3-Test5.75-8B-13.5`](https://huggingface.co/Kaoeiri/Keiana-L3-Test5.75-8B-13.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Kaoeiri/Keiana-L3-Test5.75-8B-13.5) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo Kaoeiri/Keiana-L3-Test5.75-8B-13.5-Q6_K-GGUF --model keiana-l3-test5.75-8b-13.5.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo Kaoeiri/Keiana-L3-Test5.75-8B-13.5-Q6_K-GGUF --model keiana-l3-test5.75-8b-13.5.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m keiana-l3-test5.75-8b-13.5.Q6_K.gguf -n 128
```
| {"tags": ["merge", "mergekit", "lazymergekit", "Kaoeiri/Keiana-L3-Test5.4-8B-10", "Undi95/Llama-3-LewdPlay-8B", "Kaoeiri/Keiana-L3-Test4.7-8B-3", "llama-cpp", "gguf-my-repo"], "base_model": ["Kaoeiri/Keiana-L3-Test5.4-8B-10", "Undi95/Llama-3-LewdPlay-8B", "Kaoeiri/Keiana-L3-Test4.7-8B-3"]} | Kaoeiri/Keiana-L3-Test5.75-8B-13.5-Q6_K-GGUF | null | [
"gguf",
"merge",
"mergekit",
"lazymergekit",
"Kaoeiri/Keiana-L3-Test5.4-8B-10",
"Undi95/Llama-3-LewdPlay-8B",
"Kaoeiri/Keiana-L3-Test4.7-8B-3",
"llama-cpp",
"gguf-my-repo",
"base_model:Kaoeiri/Keiana-L3-Test5.4-8B-10",
"base_model:Undi95/Llama-3-LewdPlay-8B",
"base_model:Kaoeiri/Keiana-L3-Test4.7-8B-3",
"region:us"
]
| null | 2024-04-27T22:52:45+00:00 |
null | null | {} | Code-ruto/Mental-Health-Analysis-With-BERT | null | [
"region:us"
]
| null | 2024-04-27T22:53:57+00:00 |
|
text-generation | transformers | * <span style="color:orange">I'm just tinkering. All credit to the original creator: [Undi](https://huggingface.co/Undi95).</span>
* <span style="color:orange">This model is a more traditionally quantized EXL2 compared to my usual ["rpcal" versions](https://huggingface.co/zaq-hack/Llama-3-LewdPlay-8B-evo-bpw800-h8-exl2-rpcal). Llama-3-8B seems to get markedly dumber by using the "rpcal" method. In previous models, it was difficult to tell, but the margin of error increase from quantizing Llama-3-8B makes it obvious which method is better. I deleted the lower quants of rpcal because they are pretty dumb by comparison. I recommend this version, and this version only for this model. Lower quants are significantly worse.</span>
* <span style="color:orange">This model: EXL2 @ 8.0 bpw.</span>
---
# LewdPlay-8B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
The new EVOLVE merge method was used (on MMLU specifically), see below for more information!
Unholy was used for uncensoring, Roleplay Llama 3 for the DPO train he got on top, and LewdPlay for the... lewd side.
## Prompt template: Llama3
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{output}<|eot_id|>
```
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using ./mergekit/input_models/Roleplay-Llama-3-8B_213413727 as a base.
### Models Merged
The following models were included in the merge:
* ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
* ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
dtype: bfloat16
merge_method: dare_ties
parameters:
int8_mask: 1.0
normalize: 0.0
slices:
- sources:
- layer_range: [0, 4]
model: ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
parameters:
density: 1.0
weight: 0.6861808716092435
- layer_range: [0, 4]
model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
parameters:
density: 0.6628290134113985
weight: 0.5815923052193855
- layer_range: [0, 4]
model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
parameters:
density: 1.0
weight: 0.5113886163963061
- sources:
- layer_range: [4, 8]
model: ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
parameters:
density: 0.892655547455918
weight: 0.038732602391021484
- layer_range: [4, 8]
model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
parameters:
density: 1.0
weight: 0.1982145486303527
- layer_range: [4, 8]
model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
parameters:
density: 1.0
weight: 0.6843011350690802
- sources:
- layer_range: [8, 12]
model: ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
parameters:
density: 0.7817511027396784
weight: 0.13053333213489704
- layer_range: [8, 12]
model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
parameters:
density: 0.6963703515864826
weight: 0.20525481492667985
- layer_range: [8, 12]
model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
parameters:
density: 0.6983086326765777
weight: 0.5843953969574106
- sources:
- layer_range: [12, 16]
model: ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
parameters:
density: 0.9632895768462915
weight: 0.2101146706607748
- layer_range: [12, 16]
model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
parameters:
density: 0.597557434542081
weight: 0.6728172621848589
- layer_range: [12, 16]
model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
parameters:
density: 0.756263557607837
weight: 0.2581423726361908
- sources:
- layer_range: [16, 20]
model: ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
parameters:
density: 1.0
weight: 0.2116035543552448
- layer_range: [16, 20]
model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
parameters:
density: 1.0
weight: 0.22654226422958418
- layer_range: [16, 20]
model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
parameters:
density: 0.8925914810507647
weight: 0.42243766315440867
- sources:
- layer_range: [20, 24]
model: ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
parameters:
density: 0.7697608089825734
weight: 0.1535118632140203
- layer_range: [20, 24]
model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
parameters:
density: 0.9886758076773643
weight: 0.3305040603868546
- layer_range: [20, 24]
model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
parameters:
density: 1.0
weight: 0.40670083428654535
- sources:
- layer_range: [24, 28]
model: ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
parameters:
density: 1.0
weight: 0.4542810478500622
- layer_range: [24, 28]
model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
parameters:
density: 0.8330662483310117
weight: 0.2587495367324508
- layer_range: [24, 28]
model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
parameters:
density: 0.9845313983551542
weight: 0.40378452705975915
- sources:
- layer_range: [28, 32]
model: ./mergekit/input_models/Llama-3-LewdPlay-8B-e3_2981937066
parameters:
density: 1.0
weight: 0.2951962192288415
- layer_range: [28, 32]
model: ./mergekit/input_models/Llama-3-Unholy-8B-e4_1440388923
parameters:
density: 0.960315594933433
weight: 0.13142971773782525
- layer_range: [28, 32]
model: ./mergekit/input_models/Roleplay-Llama-3-8B_213413727
parameters:
density: 1.0
weight: 0.30838472094518804
```
## Support
If you want to support me, you can [here](https://ko-fi.com/undiai). | {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["not-for-all-audiences", "nsfw", "merge"], "base_model": ["vicgalle/Roleplay-Llama-3-8B", "Undi95/Llama-3-Unholy-8B-e4", "Undi95/Llama-3-LewdPlay-8B"]} | zaq-hack/Llama-3-LewdPlay-8B-evo-8bpw-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"merge",
"conversational",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:vicgalle/Roleplay-Llama-3-8B",
"base_model:Undi95/Llama-3-Unholy-8B-e4",
"base_model:Undi95/Llama-3-LewdPlay-8B",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
]
| null | 2024-04-27T22:55:26+00:00 |
image-classification | transformers |
<!-- 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. -->
# Boya1_RMSProp_1-e5_10Epoch_swinv2-base-patch4_fold1
This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-base-patch4-window12-192-22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2928
- Accuracy: 0.6703
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1321 | 1.0 | 924 | 1.0969 | 0.6182 |
| 0.8396 | 2.0 | 1848 | 1.0085 | 0.6480 |
| 0.8851 | 3.0 | 2772 | 0.9846 | 0.6627 |
| 0.7532 | 4.0 | 3696 | 1.0034 | 0.6735 |
| 0.5372 | 5.0 | 4620 | 1.0190 | 0.6738 |
| 0.4512 | 6.0 | 5544 | 1.1292 | 0.6624 |
| 0.378 | 7.0 | 6468 | 1.1940 | 0.6594 |
| 0.3394 | 8.0 | 7392 | 1.1970 | 0.6646 |
| 0.3149 | 9.0 | 8316 | 1.2867 | 0.6684 |
| 0.2624 | 10.0 | 9240 | 1.2928 | 0.6703 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swinv2-base-patch4-window12-192-22k", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swinv2-base-patch4_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6702849389416553, "name": "Accuracy"}]}]}]} | onizukal/Boya1_RMSProp_1-e5_10Epoch_swinv2-base-patch4_fold1 | null | [
"transformers",
"safetensors",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swinv2-base-patch4-window12-192-22k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T22:56:28+00:00 |
null | null | {"license": "mit"} | camembercik/julie_kiof | null | [
"license:mit",
"region:us"
]
| null | 2024-04-27T22:56:36+00:00 |
|
text-generation | transformers |
# 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] | {"library_name": "transformers", "tags": []} | shallow6414/fmu48y0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T22:58:58+00:00 |
null | transformers |
# 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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## Environmental Impact
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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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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Mitrofazotron/llama3-1.5k_qa_3e | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T22:58:59+00:00 |
text-to-image | diffusers | {} | GraydientPlatformAPI/dreamshaper-turbo-inpainting-xl | null | [
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
]
| null | 2024-04-27T22:59:54+00:00 |
|
null | null | {"license": "mit"} | Bluebomber182/Hotel-Mario-StyleTTS2-Vokan-Model | null | [
"license:mit",
"region:us"
]
| null | 2024-04-27T23:00:12+00:00 |
|
question-answering | adapter-transformers | {"language": ["fr", "en"], "license": "apache-2.0", "library_name": "adapter-transformers", "datasets": ["lamm-mit/MechanicsMaterials", "Francesco/truck-movement", "Bikesuffer/truck25"], "metrics": ["DarrenChensformer/eval_keyphrase"], "pipeline_tag": "question-answering"} | martianband1t/integryal | null | [
"adapter-transformers",
"question-answering",
"fr",
"en",
"dataset:lamm-mit/MechanicsMaterials",
"dataset:Francesco/truck-movement",
"dataset:Bikesuffer/truck25",
"license:apache-2.0",
"region:us",
"has_space"
]
| null | 2024-04-27T23:01:05+00:00 |
|
automatic-speech-recognition | transformers |
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/bakera-sunbird/huggingface/runs/pkvclqhs)
# whisper-medium-ach
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3501
- Wer: 62.2984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.081 | 0.05 | 200 | 0.4844 | 138.8609 |
| 0.592 | 1.0248 | 400 | 0.3859 | 154.4355 |
| 0.5445 | 1.0748 | 600 | 0.3434 | 146.1694 |
| 0.3446 | 2.0495 | 800 | 0.3272 | 163.9113 |
| 0.2614 | 3.0242 | 1000 | 0.3098 | 86.2903 |
| 0.2542 | 3.0743 | 1200 | 0.3414 | 91.4315 |
| 0.1972 | 4.049 | 1400 | 0.3289 | 89.3145 |
| 0.1172 | 5.0237 | 1600 | 0.3224 | 100.1008 |
| 0.1226 | 5.0738 | 1800 | 0.3377 | 72.3286 |
| 0.0721 | 6.0485 | 2000 | 0.3277 | 105.8972 |
| 0.0504 | 7.0232 | 2200 | 0.3483 | 80.1411 |
| 0.0503 | 7.0732 | 2400 | 0.3514 | 95.0101 |
| 0.0375 | 8.048 | 2600 | 0.3378 | 64.5665 |
| 0.0348 | 9.0228 | 2800 | 0.3492 | 122.5806 |
| 0.0338 | 9.0727 | 3000 | 0.3502 | 88.6089 |
| 0.0273 | 10.0475 | 3200 | 0.3554 | 88.2560 |
| 0.0194 | 11.0222 | 3400 | 0.3501 | 62.2984 |
| 0.0165 | 11.0723 | 3600 | 0.3478 | 73.3871 |
| 0.0117 | 12.047 | 3800 | 0.3618 | 74.2440 |
| 0.0125 | 13.0218 | 4000 | 0.3587 | 97.6815 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.0
- Datasets 2.19.1.dev0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["generator"], "metrics": ["wer"], "base_model": "openai/whisper-medium", "model-index": [{"name": "whisper-medium-ach", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "generator", "type": "generator", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "wer", "value": 62.298387096774185, "name": "Wer"}]}]}]} | akera/whisper-medium-ach | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:generator",
"base_model:openai/whisper-medium",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:01:29+00:00 |
text-classification | setfit |
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| False | <ul><li>'I have so many issues to address. I have a history of sexual abuse, I’m a breast cancer survivor and I am a lifetime insomniac. I have a long history of depression and I’m beginning to have anxiety. I have low self esteem but I’ve been happily married for almost 35 years.\n I’ve never had counseling about any of this. Do I have too many issues to address in counseling?'</li><li>'I have so many issues to address. I have a history of sexual abuse, I’m a breast cancer survivor and I am a lifetime insomniac. I have a long history of depression and I’m beginning to have anxiety. I have low self esteem but I’ve been happily married for almost 35 years.\n I’ve never had counseling about any of this. Do I have too many issues to address in counseling?'</li><li>'Experiencing extreme mood swings not related to external circumstances.'</li></ul> |
| True | <ul><li>'Guide to learning a new language'</li><li>'Learning about the historical significance of the Silk Road.'</li><li>'Exploring historical landmarks in Europe'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9882 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("richie-ghost/setfit-sent-trans-mpnet-base-MH-Topic-Check")
# Run inference
preds = model("Planning a DIY home renovation project.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 4 | 33.7092 | 111 |
| Label | Training Sample Count |
|:------|:----------------------|
| True | 58 |
| False | 138 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:--------:|:-------------:|:---------------:|
| 0.0007 | 1 | 0.1327 | - |
| 0.0354 | 50 | 0.094 | - |
| 0.0708 | 100 | 0.0263 | - |
| 0.1062 | 150 | 0.0034 | - |
| 0.1415 | 200 | 0.0008 | - |
| 0.1769 | 250 | 0.0004 | - |
| 0.2123 | 300 | 0.0002 | - |
| 0.2477 | 350 | 0.0003 | - |
| 0.2831 | 400 | 0.0001 | - |
| 0.3185 | 450 | 0.0003 | - |
| 0.3539 | 500 | 0.0 | - |
| 0.3892 | 550 | 0.0002 | - |
| 0.4246 | 600 | 0.0002 | - |
| 0.4600 | 650 | 0.0001 | - |
| 0.4954 | 700 | 0.0001 | - |
| 0.5308 | 750 | 0.0001 | - |
| 0.5662 | 800 | 0.0001 | - |
| 0.6016 | 850 | 0.0001 | - |
| 0.6369 | 900 | 0.0001 | - |
| 0.6723 | 950 | 0.0001 | - |
| 0.7077 | 1000 | 0.0001 | - |
| 0.7431 | 1050 | 0.0001 | - |
| 0.7785 | 1100 | 0.0001 | - |
| 0.8139 | 1150 | 0.0001 | - |
| 0.8493 | 1200 | 0.0001 | - |
| 0.8846 | 1250 | 0.0001 | - |
| 0.9200 | 1300 | 0.0001 | - |
| 0.9554 | 1350 | 0.0001 | - |
| 0.9908 | 1400 | 0.0001 | - |
| **1.0** | **1413** | **-** | **0.0092** |
| 1.0262 | 1450 | 0.0001 | - |
| 1.0616 | 1500 | 0.0001 | - |
| 1.0970 | 1550 | 0.0 | - |
| 1.1323 | 1600 | 0.0001 | - |
| 1.1677 | 1650 | 0.0001 | - |
| 1.2031 | 1700 | 0.0001 | - |
| 1.2385 | 1750 | 0.0 | - |
| 1.2739 | 1800 | 0.0001 | - |
| 1.3093 | 1850 | 0.0 | - |
| 1.3447 | 1900 | 0.0 | - |
| 1.3800 | 1950 | 0.0001 | - |
| 1.4154 | 2000 | 0.0 | - |
| 1.4508 | 2050 | 0.0 | - |
| 1.4862 | 2100 | 0.0 | - |
| 1.5216 | 2150 | 0.0001 | - |
| 1.5570 | 2200 | 0.0 | - |
| 1.5924 | 2250 | 0.0001 | - |
| 1.6277 | 2300 | 0.0 | - |
| 1.6631 | 2350 | 0.0 | - |
| 1.6985 | 2400 | 0.0001 | - |
| 1.7339 | 2450 | 0.0 | - |
| 1.7693 | 2500 | 0.0 | - |
| 1.8047 | 2550 | 0.0 | - |
| 1.8401 | 2600 | 0.0 | - |
| 1.8754 | 2650 | 0.0 | - |
| 1.9108 | 2700 | 0.0001 | - |
| 1.9462 | 2750 | 0.0 | - |
| 1.9816 | 2800 | 0.0 | - |
| 2.0 | 2826 | - | 0.012 |
| 2.0170 | 2850 | 0.0 | - |
| 2.0524 | 2900 | 0.0 | - |
| 2.0878 | 2950 | 0.0 | - |
| 2.1231 | 3000 | 0.0 | - |
| 2.1585 | 3050 | 0.0 | - |
| 2.1939 | 3100 | 0.0 | - |
| 2.2293 | 3150 | 0.0 | - |
| 2.2647 | 3200 | 0.0 | - |
| 2.3001 | 3250 | 0.0 | - |
| 2.3355 | 3300 | 0.0 | - |
| 2.3708 | 3350 | 0.0 | - |
| 2.4062 | 3400 | 0.0 | - |
| 2.4416 | 3450 | 0.0 | - |
| 2.4770 | 3500 | 0.0 | - |
| 2.5124 | 3550 | 0.0 | - |
| 2.5478 | 3600 | 0.0 | - |
| 2.5832 | 3650 | 0.0 | - |
| 2.6185 | 3700 | 0.0001 | - |
| 2.6539 | 3750 | 0.0 | - |
| 2.6893 | 3800 | 0.0 | - |
| 2.7247 | 3850 | 0.0 | - |
| 2.7601 | 3900 | 0.0 | - |
| 2.7955 | 3950 | 0.0 | - |
| 2.8309 | 4000 | 0.0 | - |
| 2.8662 | 4050 | 0.0001 | - |
| 2.9016 | 4100 | 0.0 | - |
| 2.9370 | 4150 | 0.0 | - |
| 2.9724 | 4200 | 0.0 | - |
| 3.0 | 4239 | - | 0.0115 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.0
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
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## Model Card Authors
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> | {"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "base_model": "sentence-transformers/paraphrase-mpnet-base-v2", "widget": [{"text": "I am going through a divorce. He is extremely angry. He refuses to physically assist me with our teenager daughter. I have no extended family support. Often times, I feel overwhelmed, tired, and joyless. I feel out of control, sad and depressed on a daily basis. I am just going through the motions of life every day. I am in my mid-50s. I have almost 29 years on my job. How can I handle this?"}, {"text": "Every winter I find myself getting sad because of the weather. How can I fight this?"}, {"text": "Adjusting to life after significant life changes"}, {"text": "I have so many issues to address. I have a history of sexual abuse, I\u2019m a breast cancer survivor and I am a lifetime insomniac. I have a long history of depression and I\u2019m beginning to have anxiety. I have low self esteem but I\u2019ve been happily married for almost 35 years.\n I\u2019ve never had counseling about any of this. Do I have too many issues to address in counseling?"}, {"text": "Planning a DIY home renovation project."}], "pipeline_tag": "text-classification", "inference": true, "model-index": [{"name": "SetFit with sentence-transformers/paraphrase-mpnet-base-v2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "Unknown", "type": "unknown", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.9882352941176471, "name": "Accuracy"}]}]}]} | richie-ghost/setfit-sent-trans-mpnet-base-MH-Topic-Check | null | [
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"model-index",
"region:us"
]
| null | 2024-04-27T23:01:43+00:00 |
null | null | <!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ULUvi/Medical-gemma7b-CPT
| {} | mradermacher/Medical-gemma7b-CPT-GGUF | null | [
"region:us"
]
| null | 2024-04-27T23:02:15+00:00 |
null | transformers |
# 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
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | LeapyDeapy/whisper-small-healv1-adapters | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:03:48+00:00 |
null | null | {"license": "apache-2.0"} | TommyZQ/Mistral-1B-V2 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T23:05:53+00:00 |
|
null | null | {"license": "openrail"} | TheAmir/FrankSinatra | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T23:06:37+00:00 |
|
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | mridul161203/Meta-llama-3-8B-Qlora-quantized-GPTQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-27T23:06:43+00:00 |
null | null | {} | Rahafbk/R_Output | null | [
"tensorboard",
"region:us"
]
| null | 2024-04-27T23:07:37+00:00 |
|
null | transformers |
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{'eval_loss': 0.4338127076625824, 'eval_accuracy': 0.442, 'eval_chrf': 0.893, 'eval_bleu': 0.849, 'eval_sacreBleu': 0.8, 'eval_rouge1': 0.863, 'eval_rouge2': 0.759, 'eval_rougeL': 0.844, 'eval_rougeLsum': 0.86, 'eval_meteor': 0.813, 'eval_runtime': 113.3114, 'eval_samples_per_second': 0.035, 'eval_steps_per_second': 0.018}
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[More Information Needed] | {"library_name": "transformers", "tags": ["unsloth"]} | vdavidr/llama-3-8b-Instruct-bnb-4bit_Finetuned_usloth_dataset_size_364_epochs_10 | null | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:07:43+00:00 |
automatic-speech-recognition | transformers |
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/bakera-sunbird/huggingface/runs/c6s8tlgq)
# whisper-medium-lug
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2943
- Wer: 61.6223
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.9437 | 0.025 | 200 | 0.4902 | 427.9661 |
| 0.4586 | 1.0108 | 400 | 0.3298 | 150.3027 |
| 0.3741 | 1.0357 | 600 | 0.3337 | 143.5835 |
| 0.2659 | 2.0215 | 800 | 0.2871 | 109.6852 |
| 0.139 | 3.0072 | 1000 | 0.3437 | 131.9613 |
| 0.1734 | 3.0322 | 1200 | 0.3028 | 170.8838 |
| 0.1072 | 4.018 | 1400 | 0.2943 | 61.6223 |
| 0.0726 | 5.0038 | 1600 | 0.3438 | 114.7094 |
| 0.0751 | 5.0287 | 1800 | 0.3526 | 73.6683 |
| 0.0635 | 6.0145 | 2000 | 0.3629 | 159.7458 |
| 0.0554 | 7.0003 | 2200 | 0.3854 | 152.1186 |
| 0.0549 | 7.0252 | 2400 | 0.3751 | 98.5472 |
| 0.0283 | 8.011 | 2600 | 0.3190 | 89.2857 |
| 0.0349 | 8.036 | 2800 | 0.3452 | 155.5085 |
| 0.0379 | 9.0218 | 3000 | 0.3780 | 109.7458 |
| 0.0316 | 10.0075 | 3200 | 0.3880 | 101.4528 |
| 0.0232 | 10.0325 | 3400 | 0.4144 | 67.7966 |
| 0.0246 | 11.0183 | 3600 | 0.3820 | 71.0654 |
| 0.0192 | 12.004 | 3800 | 0.4022 | 107.6877 |
| 0.0195 | 12.029 | 4000 | 0.4276 | 126.9976 |
| 0.013 | 13.0147 | 4200 | 0.4128 | 115.3753 |
| 0.0154 | 14.0005 | 4400 | 0.4371 | 126.6949 |
| 0.0109 | 14.0255 | 4600 | 0.4213 | 142.2518 |
| 0.0133 | 15.0113 | 4800 | 0.4075 | 170.1574 |
| 0.011 | 15.0363 | 5000 | 0.4454 | 116.1622 |
| 0.0104 | 16.022 | 5200 | 0.3950 | 79.5400 |
| 0.0079 | 17.0078 | 5400 | 0.4330 | 109.2010 |
| 0.0083 | 17.0328 | 5600 | 0.4308 | 137.5303 |
| 0.0064 | 18.0185 | 5800 | 0.4178 | 96.2470 |
| 0.0057 | 19.0042 | 6000 | 0.4104 | 99.7579 |
| 0.0076 | 19.0293 | 6200 | 0.4132 | 117.0702 |
| 0.0062 | 20.015 | 6400 | 0.4404 | 146.2470 |
| 0.0035 | 21.0008 | 6600 | 0.4488 | 128.4504 |
| 0.0045 | 21.0257 | 6800 | 0.4415 | 91.0412 |
| 0.0043 | 22.0115 | 7000 | 0.4477 | 89.5884 |
| 0.0038 | 22.0365 | 7200 | 0.4550 | 82.5666 |
| 0.0028 | 23.0222 | 7400 | 0.4451 | 77.4213 |
| 0.003 | 24.008 | 7600 | 0.4424 | 78.5109 |
| 0.0033 | 24.033 | 7800 | 0.4448 | 73.4867 |
| 0.0041 | 25.0188 | 8000 | 0.4455 | 86.4407 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["generator"], "metrics": ["wer"], "base_model": "openai/whisper-medium", "model-index": [{"name": "whisper-medium-lug", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "generator", "type": "generator", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "wer", "value": 61.62227602905569, "name": "Wer"}]}]}]} | akera/whisper-medium-lug | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:generator",
"base_model:openai/whisper-medium",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:09:34+00:00 |
null | transformers |
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| {"library_name": "transformers", "tags": []} | PhillipGuo/gemma-7b_Unlearning_basketball_Lora128 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:10:12+00:00 |
null | null | {"license": "unknown"} | Thatsnazzyartist22/MysteryKinitoVoice | null | [
"license:unknown",
"region:us"
]
| null | 2024-04-27T23:12:44+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | rPucs/gemma-7b-itTripletDolly-WebNLG-fullcollator | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T23:12:52+00:00 |
text-generation | transformers | {} | wave-on-discord/llama-3-70b-llc-5-merged | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T23:14:08+00:00 |
|
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/i30alo4 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:15:23+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/xwpm4ii | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:15:28+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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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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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/op9uvqu | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:15:32+00:00 |
text-generation | transformers |
<!-- 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. -->
# my_awesome_eli5_clm-model-2
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5757
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7202 | 1.0 | 657 | 3.5844 |
| 3.6349 | 2.0 | 1314 | 3.5764 |
| 3.5929 | 3.0 | 1971 | 3.5757 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["eli5_category"], "base_model": "openai-community/gpt2", "model-index": [{"name": "my_awesome_eli5_clm-model-2", "results": []}]} | mzhou84/my_awesome_eli5_clm-model-2 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:eli5_category",
"base_model:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T23:16:41+00:00 |
text-classification | transformers |
<!-- 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. -->
# longformer-fake-review-detector-combined
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5666
- Accuracy: 0.8858
- F1: 0.8934
- Precision: 0.9933
- Recall: 0.8117
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 116 | 0.2442 | 0.9095 | 0.9184 | 0.9803 | 0.8638 |
| No log | 2.0 | 232 | 0.2785 | 0.9041 | 0.9119 | 0.9946 | 0.8419 |
| No log | 3.0 | 348 | 0.4057 | 0.9154 | 0.9232 | 0.9926 | 0.8629 |
| No log | 4.0 | 464 | 0.5666 | 0.8858 | 0.8934 | 0.9933 | 0.8117 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "allenai/longformer-base-4096", "model-index": [{"name": "longformer-fake-review-detector-combined", "results": []}]} | ibrahim-haji-abdi/longformer-fake-review-detector-combined | null | [
"transformers",
"tensorboard",
"safetensors",
"longformer",
"text-classification",
"generated_from_trainer",
"base_model:allenai/longformer-base-4096",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:17:53+00:00 |
null | peft |
<!-- 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. -->
# llama3-sft-pc-v1
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.14.7
- Tokenizers 0.19.1 | {"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "llama3-sft-pc-v1", "results": []}]} | BoooomNing/llama3-sft-pc-v1 | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
]
| null | 2024-04-27T23:21:28+00:00 |
text-to-image | diffusers | {} | GraydientPlatformAPI/duc-art3-xl-proper | null | [
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
]
| null | 2024-04-27T23:23:27+00:00 |
|
null | null | {} | kiwiee/JohnLennon | null | [
"region:us"
]
| null | 2024-04-27T23:23:46+00:00 |
|
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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<!-- 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. -->
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[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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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | gaurav021201/Meta-Llama-3-8B-Qlora-GPTQ | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:24:08+00:00 |
feature-extraction | transformers | This model is a fine-tuned version of https://huggingface.co/NbAiLab/nb-sbert-base. All credit for the model nb-sbert-base goes to the authors, Rolv-Arild Braaten and Per Egil Kummervold.
Our model is fine-tuned to become good at recognizing a set of 37 questions. This was done using set-fit. The training data consists of 6x37 questions, where each question is a reformulation of one of the 37 questions.
The questions were paired with the question they were made from during training. | {"license": "cc-by-4.0"} | DiffuseCalmly/BachelorSBERT | null | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:25:26+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** zz-xx
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | zz-xx/llama-3-8b-bnb-4bit-bias-detection-16-bit | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:25:38+00:00 |
text-generation | transformers |
# 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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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## 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]
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<!-- 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
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/auj2vxc | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T23:26:24+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/DavidAU/D_AU-Orac-13B-Tiefighter-slerp
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF/resolve/main/D_AU-Orac-13B-Tiefighter-slerp.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF/resolve/main/D_AU-Orac-13B-Tiefighter-slerp.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF/resolve/main/D_AU-Orac-13B-Tiefighter-slerp.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF/resolve/main/D_AU-Orac-13B-Tiefighter-slerp.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF/resolve/main/D_AU-Orac-13B-Tiefighter-slerp.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF/resolve/main/D_AU-Orac-13B-Tiefighter-slerp.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF/resolve/main/D_AU-Orac-13B-Tiefighter-slerp.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF/resolve/main/D_AU-Orac-13B-Tiefighter-slerp.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF/resolve/main/D_AU-Orac-13B-Tiefighter-slerp.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF/resolve/main/D_AU-Orac-13B-Tiefighter-slerp.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF/resolve/main/D_AU-Orac-13B-Tiefighter-slerp.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF/resolve/main/D_AU-Orac-13B-Tiefighter-slerp.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF/resolve/main/D_AU-Orac-13B-Tiefighter-slerp.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF/resolve/main/D_AU-Orac-13B-Tiefighter-slerp.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": "DavidAU/D_AU-Orac-13B-Tiefighter-slerp", "quantized_by": "mradermacher"} | mradermacher/D_AU-Orac-13B-Tiefighter-slerp-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:DavidAU/D_AU-Orac-13B-Tiefighter-slerp",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:26:59+00:00 |
null | null | {} | kiwiee/paul | null | [
"region:us"
]
| null | 2024-04-27T23:31:13+00:00 |
|
null | peft |
<!-- 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. -->
# codeT5-small-context
This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8472
## 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: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.1783 | 1.0 | 4383 | 0.9338 |
| 1.0719 | 2.0 | 8766 | 0.8854 |
| 1.0357 | 3.0 | 13149 | 0.8605 |
| 1.0336 | 4.0 | 17532 | 0.8515 |
| 0.9942 | 5.0 | 21915 | 0.8472 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "Salesforce/codet5-small", "model-index": [{"name": "codeT5-small-context", "results": []}]} | EEsu/codeT5-small-with-lora-without-prompt | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:Salesforce/codet5-small",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T23:31:37+00:00 |
null | null | {} | joacorf33/xlm-roberta-base-finetuned-panx-de-fr | null | [
"region:us"
]
| null | 2024-04-27T23:33:07+00:00 |
|
token-classification | allennlp |
# Distilbert CoBaLD parser
This version of parser is based on distilbert encoder. The encoder weights were initialized from `distilbert-base-uncased` model.
## Usage
1. Clone https://github.com/CobaldAnnotation/CobaldParser, go to the repo directory and install requirements.
2. Download the `model.tag.gz` file. You can do it manually of via `git clone https://huggingface.co/CoBaLD/distilbert-cobald-parser`.
3. Run `allennlp predict` with the following arguments on the conllu file you want to annotate with CoBaLD:
```
allennlp predict <PATH_TO_model.tar.gz> <INPUT_CONLLU_FILE> \
--output-file <OUTPUT_CONLLU_FILE> \
--include-package src \
--predictor morpho_syntax_semantic_predictor \
--use-dataset-reader
```
4. Now see the results in output conllu file you specified.
| {"language": ["en"], "library_name": "allennlp", "pipeline_tag": "token-classification"} | CoBaLD/distilbert-cobald-parser | null | [
"allennlp",
"tensorboard",
"token-classification",
"en",
"region:us"
]
| null | 2024-04-27T23:35:09+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Nous-Hermes-2-Mixtral-8x7B-SFT - bnb 4bits
- Model creator: https://huggingface.co/NousResearch/
- Original model: https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT/
Original model description:
---
base_model: mistralai/Mixtral-8x7B-v0.1
tags:
- Mixtral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
model-index:
- name: Nous-Hermes-2-Mixtral-8x7B-SFT
results: []
license: apache-2.0
language:
- en
datasets:
- teknium/OpenHermes-2.5
---
# Nous Hermes 2 - Mixtral 8x7B - SFT
## Model description
Nous Hermes 2 Mixtral 8x7B SFT is the supervised finetune only version of our new flagship Nous Research model trained over the [Mixtral 8x7B MoE LLM](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1).
The model was trained on over 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape, achieving state of the art performance on a variety of tasks.
This is the SFT only version of Mixtral Hermes 2, we have also released an SFT+DPO version, for people to find which works best for them, which can be found here: https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO
## We are grateful to Together.ai for sponsoring our compute during the many experiments both training Mixtral and working on DPO!
# Table of Contents
1. [Example Outputs](#example-outputs)
2. [Benchmark Results](#benchmark-results)
- GPT4All
- AGIEval
- BigBench
- Comparison to Mixtral-Instruct
3. [Prompt Format](#prompt-format)
4. [Inference Example Code](#inference-code)
5. [Quantized Models](#quantized-models)
## Example Outputs
### Writing Code for Data Visualization
### Writing Cyberpunk Psychedelic Poems
### Performing Backtranslation to Create Prompts from Input Text
## Benchmark Results
Nous-Hermes 2 on Mixtral 8x7B SFT is the bedrock for major improvements on many of the benchmarks below compared to the base Mixtral model, and is the SFT only version of our first model to beat the flagship Mixtral Finetune by MistralAI (the DPO version).
## GPT4All:
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5904|± |0.0144|
| | |acc_norm|0.6323|± |0.0141|
|arc_easy | 0|acc |0.8594|± |0.0071|
| | |acc_norm|0.8607|± |0.0071|
|boolq | 1|acc |0.8783|± |0.0057|
|hellaswag | 0|acc |0.6592|± |0.0047|
| | |acc_norm|0.8434|± |0.0036|
|openbookqa | 0|acc |0.3400|± |0.0212|
| | |acc_norm|0.4660|± |0.0223|
|piqa | 0|acc |0.8324|± |0.0087|
| | |acc_norm|0.8379|± |0.0086|
|winogrande | 0|acc |0.7569|± |0.0121|
```
Average: 75.36
## AGIEval:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.2441|± |0.0270|
| | |acc_norm|0.2598|± |0.0276|
|agieval_logiqa_en | 0|acc |0.4025|± |0.0192|
| | |acc_norm|0.3978|± |0.0192|
|agieval_lsat_ar | 0|acc |0.2391|± |0.0282|
| | |acc_norm|0.2043|± |0.0266|
|agieval_lsat_lr | 0|acc |0.5353|± |0.0221|
| | |acc_norm|0.5098|± |0.0222|
|agieval_lsat_rc | 0|acc |0.6617|± |0.0289|
| | |acc_norm|0.5948|± |0.0300|
|agieval_sat_en | 0|acc |0.7961|± |0.0281|
| | |acc_norm|0.7816|± |0.0289|
|agieval_sat_en_without_passage| 0|acc |0.4757|± |0.0349|
| | |acc_norm|0.4515|± |0.0348|
|agieval_sat_math | 0|acc |0.4818|± |0.0338|
| | |acc_norm|0.3909|± |0.0330|
```
Average: 44.89
## BigBench:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.5789|± |0.0359|
|bigbench_date_understanding | 0|multiple_choice_grade|0.7154|± |0.0235|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.5388|± |0.0311|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.4680|± |0.0264|
| | |exact_str_match |0.0000|± |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.3260|± |0.0210|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2443|± |0.0163|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.5233|± |0.0289|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.3700|± |0.0216|
|bigbench_navigate | 0|multiple_choice_grade|0.5000|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.6665|± |0.0105|
|bigbench_ruin_names | 0|multiple_choice_grade|0.6317|± |0.0228|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2505|± |0.0137|
|bigbench_snarks | 0|multiple_choice_grade|0.7127|± |0.0337|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.6592|± |0.0151|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.6860|± |0.0147|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2200|± |0.0117|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1503|± |0.0085|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.5233|± |0.0289|
```
Average: 48.69
# Benchmark Comparison Charts
## GPT4All
## AGI-Eval
## BigBench Reasoning Test
# Prompt Format
Nous Hermes 2 uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
```
This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
When quantized versions of the model are released, I recommend using LM Studio for chatting with Nous Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
In LM-Studio, simply select the ChatML Prefix on the settings side pane:
# Inference Code
Here is example code using HuggingFace Transformers to inference the model (note: even in 4bit, it will require more than 24GB of VRAM)
```python
# Code to inference Hermes with HF Transformers
# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import LlamaTokenizer, MixtralForCausalLM
import bitsandbytes, flash_attn
tokenizer = LlamaTokenizer.from_pretrained('NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO', trust_remote_code=True)
model = MixtralForCausalLM.from_pretrained(
"NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
load_in_4bit=True,
use_flash_attention_2=True
)
prompts = [
"""<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
<|im_start|>assistant""",
]
for chat in prompts:
print(chat)
input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
print(f"Response: {response}")
```
# Quantized Models:
## All sizes of GGUF Quantizations are available here:
### SFT+DPO Version - https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF
### SFT Only Version - https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT-GGUF
(Note: If you have issues with these GGUF's try TheBloke's)
## TheBloke has also quantized Hermes Mixtral in various forms:
### SFT+DPO GGUF: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-GGUF
### SFT GGUF: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GGUF
### SFT+DPO GPTQ: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-GPTQ
### SFT GPTQ: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GPTQ
### SFT+DPO AWQ: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-DPO-AWQ
### SFT AWQ: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-AWQ
## There is also an MLX version available:
### https://huggingface.co/mlx-community/Nous-Hermes-2-Mixtral-8x7B-DPO-4bit
## Exllama2 quants available here:
### https://huggingface.co/qeternity/Nous-Hermes-2-Mixtral-8x7B-SFT-4bpw-h6-exl2
(other sizes available in Qeternity's repos)
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
| {} | RichardErkhov/NousResearch_-_Nous-Hermes-2-Mixtral-8x7B-SFT-4bits | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-27T23:35:49+00:00 |
text-generation | transformers | 6.9bpw exl2 quant of https://huggingface.co/TheDrummer/Llama-3SOME-8B-v1-BETA | {} | MarsupialAI/Llama-3SOME-8B-v1-BETA_6.9bpw_exl2 | null | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T23:38:07+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** zz-xx
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | zz-xx/llama-3-8b-bnb-4bit-bias-detection-lora | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:40:19+00:00 |
null | null | {"license": "cc"} | ItsJustKiri919/1 | null | [
"license:cc",
"region:us"
]
| null | 2024-04-27T23:41:51+00:00 |
|
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/ky29crm | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:42:22+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/hxha9c8 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:42:27+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/o46be3b | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:42:31+00:00 |
null | transformers |
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| {"library_name": "transformers", "tags": []} | PhillipGuo/gemma-7b_Unlearning_basketball_Maintain_football_Lora128 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:43:52+00:00 |
image-classification | transformers |
<!-- 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. -->
# Boya1_RMSProp_1-e5_10Epoch_swinv2-base-patch4_fold2
This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-base-patch4-window12-192-22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2511
- Accuracy: 0.6743
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1739 | 1.0 | 923 | 1.1721 | 0.5968 |
| 1.0295 | 2.0 | 1846 | 1.0306 | 0.6524 |
| 0.7614 | 3.0 | 2769 | 0.9601 | 0.6719 |
| 0.7933 | 4.0 | 3692 | 0.9725 | 0.6730 |
| 0.7602 | 5.0 | 4615 | 1.0029 | 0.6643 |
| 0.5166 | 6.0 | 5538 | 1.0468 | 0.6759 |
| 0.3744 | 7.0 | 6461 | 1.1294 | 0.6678 |
| 0.3112 | 8.0 | 7384 | 1.1635 | 0.6730 |
| 0.3642 | 9.0 | 8307 | 1.2528 | 0.6765 |
| 0.3021 | 10.0 | 9230 | 1.2511 | 0.6743 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swinv2-base-patch4-window12-192-22k", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swinv2-base-patch4_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6743243243243243, "name": "Accuracy"}]}]}]} | onizukal/Boya1_RMSProp_1-e5_10Epoch_swinv2-base-patch4_fold2 | null | [
"transformers",
"safetensors",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swinv2-base-patch4-window12-192-22k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:44:47+00:00 |
text-generation | transformers |
# 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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[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
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | tom-brady/6-243 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:45:36+00:00 |
text-generation | transformers | {} | mfaizanakhtar/Llama-2-python-Faizan-ModelD | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T23:47:29+00:00 |
|
feature-extraction | transformers |
# 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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## 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. -->
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[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]
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#### Preprocessing [optional]
[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | andersonbcdefg/tiny-emb-2024-04-27_23-49-00 | null | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:49:00+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_total_Instruction0_APSOL_v1_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_total_Instruction0_APSOL_v1_h1", "results": []}]} | ThuyNT/CS505_COQE_viT5_total_Instruction0_APSOL_v1_h1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T23:55:01+00:00 |
null | peft | ---
# Model Card for Khalsa
<!-- Provide a quick summary of what the model is/does. [Optional] -->
Fine-tuned Gemma Model which was worked on using the intel developer cloud, and trained on using Intel Max 1550 GPU
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
Fine-tuned Gemma Model which was worked on using the intel developer cloud
- **Developed by:** Manik Sethi, Britney Nguyen, Mario Miranda
- **Model type:** Language model
- **Language(s) (NLP):** eng
- **License:** apache-2.0
- **Parent Model:** gemma-2b
- **Resources for more information:** [Intel Develpor Cloud](https://console.cloud.intel.com/training)
# 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. -->
Model is intended to be used by individuals who are struggling to understand the information in important documentations. More specifically, the demographic includes immigrants and visa holders who struggle with english. When they receive documentaiton from jobs, government agencies, or healthcare, our model should be able to answer any questions they have.
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
User uploads a pdf to the application, which is then parsed by our model. The user is then able to ask questions about content in the given documentation.
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
Misuse of the model would entail relying on it to provide legal advice, which it is not intended to give.
# Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Current limitations are the quantity of languages available for the model to serve in.
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
To translate the advice into a target language, we suggest first taking the output from the LLM, and *then* translating it. Trying to get the model to do both simultaneously may result in flawed responses.
# Training Details
## Training Data
Model was trained using the [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) datbase. This dataset contains a diverse range of question-answer pairs spanning multiple categories, facilitating comprehensive training. By focusing specifically on the question-answer pairs, the model adapts to provide accurate and relevant responses to various inquiries.
<!-- This should link to a Data 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. -->
## 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
The dataset underwent preprocessing steps to extract question-answer pairs relevant to the "Question answering" category. This involved filtering the dataset to ensure that the model is fine-tuned on pertinent data, enhancing its ability to provide accurate responses.
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
Ran through 25 epocs.
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
We fed the following prompts into the model
<!-- This should link to a Data Card if possible. -->
"What are the main differences between a vegetarian and a vegan diet?",
"What are some effective strategies for managing stress and anxiety?",
"Can you explain the concept of blockchain technology in simple terms?",
"What are the key factors that influence the price of crude oil in global markets?",
"When did Virgin Australia start operating?"
## Results
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:** Intel XEON hardware
- **Hours used:** More information needed
- **Cloud Provider:** Intel Developer cloud
- **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
Trained model on Intel Max 1550 GPU
### Software
Developed model using Intel Developer Cloud
# Model Card Authors
Manik Sethi, Britney Nguyen, Mario Miranda
# Model Card Contact
More information needed
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
More information needed
</details>
| {"license": "gemma", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "google/gemma-2b", "model-index": [{"name": "gemma-2b-dolly-qa", "results": []}]} | mksethi/khalsaa | null | [
"peft",
"safetensors",
"gemma",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
]
| null | 2024-04-27T23:55:21+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_total_Instruction0_OPSAL_v1_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_total_Instruction0_OPSAL_v1_h1", "results": []}]} | ThuyNT/CS505_COQE_viT5_total_Instruction0_OPSAL_v1_h1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T23:55:55+00:00 |
null | peft |
<!-- 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. -->
# 20240427_finetuned-llava-1.5-7b-hf_full_dataset
This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.2
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "llava-hf/llava-1.5-7b-hf", "model-index": [{"name": "20240427_finetuned-llava-1.5-7b-hf_full_dataset", "results": []}]} | samyak24jain/20240427_finetuned-llava-1.5-7b-hf_full_dataset | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:llava-hf/llava-1.5-7b-hf",
"region:us"
]
| null | 2024-04-27T23:55:59+00:00 |
text-generation | transformers |
# 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]
| {"library_name": "transformers", "tags": []} | PhillipGuo/gemma-2b_Unlearning_basketball_Maintain_football | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T23:56:08+00:00 |
image-classification | transformers | {} | onizukal/Boya1_RMSProp_1-e5_10Epoch_swinv2-small-patch4-window16-256_fold5 | null | [
"transformers",
"safetensors",
"swinv2",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T23:57:01+00:00 |
|
null | null | lora名のaa～がトリガーワードです
詳しくはpngのプロンプトを参考してください
| {"license": "unknown"} | shinkuukan/chara | null | [
"license:unknown",
"region:us"
]
| null | 2024-04-27T23:57:13+00:00 |
null | null | {"license": "openrail"} | johnmoley22/Art300EpochToma | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T23:57:16+00:00 |
|
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: HusseinEid/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | HusseinEid/ppo-PyramidsRND | null | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| null | 2024-04-27T23:59:38+00:00 |
text-generation | transformers |
# 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
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#### 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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/oxbrodl | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T00:00:24+00:00 |
text-generation | transformers |
# Vicuna Model Card
## Model Details
Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
- **Developed by:** [LMSYS](https://lmsys.org/)
- **Model type:** An auto-regressive language model based on the transformer architecture.
- **License:** Non-commercial license
- **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971).
### Model Sources
- **Repository:** https://github.com/lm-sys/FastChat
- **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/
- **Paper:** https://arxiv.org/abs/2306.05685
- **Demo:** https://chat.lmsys.org/
## Uses
The primary use of Vicuna is research on large language models and chatbots.
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
## How to Get Started with the Model
- Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights.
- APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api.
## Training Details
Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning.
The training data is around 125K conversations collected from ShareGPT.com.
See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf).
## Evaluation
Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).
## Difference between different versions of Vicuna
See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md) | {"inference": false} | sirovub/vicuna-33b-v1.3-GGUF | null | [
"transformers",
"pytorch",
"gguf",
"llama",
"text-generation",
"arxiv:2302.13971",
"arxiv:2306.05685",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T00:00:24+00:00 |
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