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# Model Card for Model ID
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### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "meta-llama/Meta-Llama-3-8B"} | LazyCatAI/llama-3-8b-test | null | [
"peft",
"safetensors",
"gguf",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B",
"region:us"
]
| null | 2024-04-28T04:41:33+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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Uses
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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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#### Speeds, Sizes, Times [optional]
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#### Summary
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## Environmental Impact
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- **Hardware Type:** [More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/ecnicir | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T04:43:04+00:00 |
null | null | {} | PQlet/results | null | [
"region:us"
]
| null | 2024-04-28T04:44:36+00:00 |
|
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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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 -->
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### Testing Data, Factors & Metrics
#### Testing Data
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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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## Technical Specifications [optional]
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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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[More Information Needed]
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### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "t5-base"} | PQlet/T5base-lora-sumarizationTables-v2-aug2-PermuteCols-trainer | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:t5-base",
"region:us"
]
| null | 2024-04-28T04:45:27+00:00 |
null | transformers |
# DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q8_0-GGUF
This model was converted to GGUF format from [`DavidAU/D_AU-Orac-13B-Tiefighter-slerp`](https://huggingface.co/DavidAU/D_AU-Orac-13B-Tiefighter-slerp) 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/DavidAU/D_AU-Orac-13B-Tiefighter-slerp) 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 DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q8_0-GGUF --model d_au-orac-13b-tiefighter-slerp.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q8_0-GGUF --model d_au-orac-13b-tiefighter-slerp.Q8_0.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 d_au-orac-13b-tiefighter-slerp.Q8_0.gguf -n 128
```
| {"library_name": "transformers", "tags": ["mergekit", "merge", "llama-cpp", "gguf-my-repo"], "base_model": ["microsoft/Orca-2-13b", "KoboldAI/LLaMA2-13B-Tiefighter"]} | DavidAU/D_AU-Orac-13B-Tiefighter-slerp-Q8_0-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:microsoft/Orca-2-13b",
"base_model:KoboldAI/LLaMA2-13B-Tiefighter",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T04:46:30+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** gromoboy
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "gguf"], "base_model": "unsloth/mistral-7b-bnb-4bit"} | gromoboy/mistral_gguf | null | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T04:48:43+00:00 |
text-generation | null |
# seawolf2357/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) 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/meta-llama/Meta-Llama-3-8B-Instruct) 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 seawolf2357/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF --model meta-llama-3-8b-instruct.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo seawolf2357/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF --model meta-llama-3-8b-instruct.Q4_K_M.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 meta-llama-3-8b-instruct.Q4_K_M.gguf -n 128
```
| {"language": ["en"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo"], "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", "widget": [{"example_title": "Hello", "messages": [{"role": "user", "content": "Hey my name is Julien! How are you?"}]}, {"example_title": "Winter holidays", "messages": [{"role": "system", "content": "You are a helpful and honest assistant. Please, respond concisely and truthfully."}, {"role": "user", "content": "Can you recommend a good destination for Winter holidays?"}]}, {"example_title": "Programming assistant", "messages": [{"role": "system", "content": "You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully."}, {"role": "user", "content": "Write a function that computes the nth fibonacci number."}]}], "inference": {"parameters": {"max_new_tokens": 300, "stop": ["<|end_of_text|>", "<|eot_id|>"]}}} | seawolf2357/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF | null | [
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"license:other",
"region:us"
]
| null | 2024-04-28T04:51:44+00:00 |
null | null | {} | kandarpraval/costal | null | [
"region:us"
]
| null | 2024-04-28T04:53:04+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** edpowers
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "gguf"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"} | edpowers/mistral_7b_instruct_v2_quant_v2 | null | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T04:53:13+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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/5gh4l3g | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T04:55:26+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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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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]
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[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]
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#### Testing Data
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/f06x92d | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T04:55:31+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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
### Out-of-Scope Use
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## 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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- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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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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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/z5l6c9z | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T04:55:38+00:00 |
null | null | {} | manoj-dhakal/llama-3-8b-PhiloSloppy-Socrates | null | [
"region:us"
]
| null | 2024-04-28T04:56:00+00:00 |
|
text-generation | transformers | # ๐ Llama-3-Open-Ko-Linear-8B
## ๐๏ธ Merge Details
"I thought about it yesterdayโmerging the solid foundation of beomi/Llama-3-Open-Ko-8B with the specialized precision of beomi/Llama-3-Open-Ko-8B-Instruct-preview, using task arithmetic, is like composing a korean song that seamlessly blends timeless rhythms with contemporary solos, creating a harmonious masterpiece tailored to today's needs."
### ๐ฐ๐ท Merge Method
This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using [beomi/Llama-3-Open-Ko-8B](https://huggingface.co/beomi/Llama-3-Open-Ko-8B) as a base.
### ๐ฐ๐ท Models Merged
The following models were included in the merge:
* [beomi/Llama-3-Open-Ko-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview)
### ๐พ Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- layer_range: [0, 31]
model: beomi/Llama-3-Open-Ko-8B
parameters:
weight: 0.2
- layer_range: [0, 31]
model: beomi/Llama-3-Open-Ko-8B-Instruct-preview
parameters:
weight: 0.8
merge_method: task_arithmetic
base_model: beomi/Llama-3-Open-Ko-8B
dtype: bfloat16
random_seed: 0
```
| {"license": "other", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["beomi/Llama-3-Open-Ko-8B-Instruct-preview", "beomi/Llama-3-Open-Ko-8B"]} | asiansoul/Llama-3-Open-Ko-Linear-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"arxiv:2212.04089",
"base_model:beomi/Llama-3-Open-Ko-8B-Instruct-preview",
"base_model:beomi/Llama-3-Open-Ko-8B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T04:57:12+00:00 |
text-generation | transformers | Quantizations of https://huggingface.co/CreitinGameplays/bloom-3b-conversational
# From original readme
**Specific Input Format:**
The model was fine-tuned using a specific input format that goes like this:
```
<|system|> {system prompt} </s> <|prompter|> {user prompt} </s> <|assistant|> {model response} ```
Using this format when interacting with the model can improve its performance and generate more relevant responses.
| {"language": ["en"], "license": "other", "tags": ["transformers", "gguf", "bloom-3b-conversational"], "pipeline_tag": "text-generation", "inference": false} | duyntnet/bloom-3b-conversational-GGUF | null | [
"transformers",
"gguf",
"bloom-3b-conversational",
"text-generation",
"en",
"license:other",
"region:us"
]
| null | 2024-04-28T04:57:18+00:00 |
question-answering | 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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1542
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.1995 | 1.0 | 5533 | 1.1654 |
| 0.9383 | 2.0 | 11066 | 1.1052 |
| 0.7473 | 3.0 | 16599 | 1.1542 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | yweslakarep/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T04:57:45+00:00 |
null | null | {"license": "openrail"} | BunnyToon/mundodotorajo | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-28T04:58:32+00:00 |
|
image-feature-extraction | transformers |
Description
This is a fine tuned google/siglip-so400m-patch14-384 for the purpose of quantizing the embeddings to binary. It's only using the first 1024 embeddings, so if you use all 1152 of them your results will be less than desirable.
I updated the model today (April 30th) and evals are much better than before, but I'm continuing training so perf should only get better from here.
Evals
Coming soon | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers"} | carsonpoole/binary-siglip-vision | null | [
"transformers",
"safetensors",
"siglip_vision_model",
"image-feature-extraction",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T04:58:42+00:00 |
text-generation | transformers |
# zephyr-7b-alpha-ExPO
The extrapolated (ExPO) model based on `HuggingFaceH4/zephyr-7b-alpha` and `HuggingFaceH4/mistral-7b-sft-alpha`, as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper.
Specifically, we obtain this model by extrapolating from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. | {"language": ["en"], "license": "apache-2.0"} | chujiezheng/zephyr-7b-alpha-ExPO | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"arxiv:2404.16792",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T04:59:49+00:00 |
null | transformers |
# Description
This is a fine tuned `google/siglip-so400m-patch14-384` for the purpose of quantizing the embeddings to binary.
It's only using the first 1024 embeddings, so if you use all 1152 of them your results will be less than desirable.
I updated the model today (April 30th) and evals are much better than before, but I'm continuing training so perf should only get better from here.
## Evals
Coming soon | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers"} | carsonpoole/binary-siglip-text | null | [
"transformers",
"safetensors",
"siglip_text_model",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:00:55+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/maywell/PiVoT-0.1-Evil-a
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-GGUF
## 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/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/PiVoT-0.1-Evil-a-i1-GGUF/resolve/main/PiVoT-0.1-Evil-a.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K |
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"], "license": "cc-by-sa-4.0", "library_name": "transformers", "tags": ["not-for-all-audiences"], "datasets": ["maywell/ko_wikidata_QA", "kyujinpy/OpenOrca-KO", "Anthropic/hh-rlhf"], "base_model": "maywell/PiVoT-0.1-Evil-a", "quantized_by": "mradermacher"} | mradermacher/PiVoT-0.1-Evil-a-i1-GGUF | null | [
"transformers",
"gguf",
"not-for-all-audiences",
"en",
"dataset:maywell/ko_wikidata_QA",
"dataset:kyujinpy/OpenOrca-KO",
"dataset:Anthropic/hh-rlhf",
"base_model:maywell/PiVoT-0.1-Evil-a",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:01:53+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/chujiezheng/tulu-2-dpo-13b-ExPO
<!-- 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/tulu-2-dpo-13b-ExPO-GGUF/resolve/main/tulu-2-dpo-13b-ExPO.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-13b-ExPO-GGUF/resolve/main/tulu-2-dpo-13b-ExPO.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-13b-ExPO-GGUF/resolve/main/tulu-2-dpo-13b-ExPO.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-13b-ExPO-GGUF/resolve/main/tulu-2-dpo-13b-ExPO.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-13b-ExPO-GGUF/resolve/main/tulu-2-dpo-13b-ExPO.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-13b-ExPO-GGUF/resolve/main/tulu-2-dpo-13b-ExPO.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-13b-ExPO-GGUF/resolve/main/tulu-2-dpo-13b-ExPO.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-13b-ExPO-GGUF/resolve/main/tulu-2-dpo-13b-ExPO.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-13b-ExPO-GGUF/resolve/main/tulu-2-dpo-13b-ExPO.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-13b-ExPO-GGUF/resolve/main/tulu-2-dpo-13b-ExPO.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-13b-ExPO-GGUF/resolve/main/tulu-2-dpo-13b-ExPO.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-13b-ExPO-GGUF/resolve/main/tulu-2-dpo-13b-ExPO.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-13b-ExPO-GGUF/resolve/main/tulu-2-dpo-13b-ExPO.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-13b-ExPO-GGUF/resolve/main/tulu-2-dpo-13b-ExPO.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"], "license": "other", "library_name": "transformers", "base_model": "chujiezheng/tulu-2-dpo-13b-ExPO", "license_link": "https://allenai.org/impact-license", "license_name": "ai2-impact-license-low-risk", "quantized_by": "mradermacher"} | mradermacher/tulu-2-dpo-13b-ExPO-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:chujiezheng/tulu-2-dpo-13b-ExPO",
"license:other",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:03:19+00:00 |
null | null | {"license": "creativeml-openrail-m"} | casque/peplumtop-20 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2024-04-28T05:04:05+00:00 |
|
null | null | {} | ishaqpaktin/donut_blender | null | [
"region:us"
]
| null | 2024-04-28T05:05:02+00:00 |
|
null | null | {"license": "apache-2.0"} | AdnanMajeed/Documentary | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-28T05:05:05+00:00 |
|
null | null | {} | AdnanMajeed/d | null | [
"region:us"
]
| null | 2024-04-28T05:05:16+00:00 |
|
text2text-generation | transformers | {} | anhmanucian1903/vit5-base-finetuned-VN | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:07:54+00:00 |
|
null | null | {"license": "apache-2.0"} | Jaypen/ENHYPEN_models_by_HG0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-28T05:08:44+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 ๐ค 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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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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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<!-- 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]
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## Technical Specifications [optional]
### Model Architecture and Objective
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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<!-- 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/xp39rq9 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:08:51+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. -->
# tulu2-7b-cost-UI-both
# UI coherence 10k + UI correctness 10k
This model is a fine-tuned version of [allenai/tulu-2-7b](https://huggingface.co/allenai/tulu-2-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6883
- Rewards/chosen: -0.1974
- Rewards/rejected: -0.2211
- Rewards/accuracies: 0.5370
- Rewards/margins: 0.0236
- Rewards/margins Max: 0.3503
- Rewards/margins Min: -0.2527
- Rewards/margins Std: 0.1981
- Logps/rejected: -356.2906
- Logps/chosen: -363.1418
- Logits/rejected: 0.9920
- Logits/chosen: 0.8393
## 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-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Rewards/margins Max | Rewards/margins Min | Rewards/margins Std | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:-------------------:|:-------------------:|:-------------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.2381 | 1.0 | 578 | 0.6883 | -0.1974 | -0.2211 | 0.5370 | 0.0236 | 0.3503 | -0.2527 | 0.1981 | -356.2906 | -363.1418 | 0.9920 | 0.8393 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "allenai/tulu-2-7b", "model-index": [{"name": "tulu2-7b-cost-UI-both", "results": []}]} | just1nseo/tulu2-7b-cost-UI-both | null | [
"peft",
"safetensors",
"llama",
"trl",
"dpo",
"generated_from_trainer",
"base_model:allenai/tulu-2-7b",
"4-bit",
"region:us"
]
| null | 2024-04-28T05:09:16+00:00 |
text-classification | transformers | {} | nruigrok/NLP_NLI_Success | null | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:09:54+00:00 |
|
text-generation | transformers |
# zephyr_0.1_a8.0
The extrapolated (ExPO) model based on `chujiezheng/zephyr_0.1` and `alignment-handbook/zephyr-7b-sft-full`, as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper.
Specifically, we obtain this model by extrapolating from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. | {"language": ["en"], "license": "apache-2.0"} | chujiezheng/zephyr_0.1_a8.0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"arxiv:2404.16792",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:11:02+00:00 |
null | null | {"license": "openrail"} | KeroroK66/Viblos | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-28T05:11:51+00:00 |
|
null | null | {"license": "creativeml-openrail-m"} | casque/1.5_perfect_hands | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2024-04-28T05:12:12+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 ๐ค 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
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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
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Ynir/llama-3-8b-instruct-test-v1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:12:33+00:00 |
null | null | {"license": "openrail"} | KeroroK66/Cheval | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-28T05:13:06+00:00 |
|
null | null | {"license": "openrail"} | mijkim/therapist-falcom | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-28T05:13:23+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** baconnier
- **License:** apache-2.0
- **Finetuned from model :** cognitivecomputations/dolphin-2.9-llama3-8b
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", "gguf"], "base_model": "cognitivecomputations/dolphin-2.9-llama3-8b"} | baconnier/finance_dolphin_orpo_llama3_8B_r64_51K_GGUF | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:cognitivecomputations/dolphin-2.9-llama3-8b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"has_space"
]
| null | 2024-04-28T05:13:26+00:00 |
null | null | {"license": "openrail"} | KeroroK66/Rickey | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-28T05:15:35+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 ๐ค 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]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[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]
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[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | janani4office2/akam_product_NER_mistral-7b-4bit | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:15:39+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. -->
# financeLM_outputpath_Sentiment_Analysis_Balanced__15
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5191
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0424 | 1.0 | 358 | 1.6885 |
| 1.3339 | 2.0 | 717 | 1.7008 |
| 1.0278 | 3.0 | 1076 | 1.7622 |
| 0.819 | 4.0 | 1435 | 1.8862 |
| 0.6674 | 5.0 | 1793 | 2.0067 |
| 0.5544 | 6.0 | 2152 | 2.1500 |
| 0.4702 | 7.0 | 2511 | 2.2106 |
| 0.4061 | 8.0 | 2870 | 2.3040 |
| 0.3599 | 9.0 | 3228 | 2.3646 |
| 0.3226 | 10.0 | 3587 | 2.4215 |
| 0.2939 | 11.0 | 3946 | 2.4431 |
| 0.2728 | 12.0 | 4305 | 2.4787 |
| 0.2577 | 13.0 | 4663 | 2.4998 |
| 0.2442 | 14.0 | 5022 | 2.5109 |
| 0.2368 | 14.97 | 5370 | 2.5191 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "openai-community/gpt2", "model-index": [{"name": "financeLM_outputpath_Sentiment_Analysis_Balanced__15", "results": []}]} | Supersaiyan1729/financeLM_outputpath_Sentiment_Analysis_Balanced__15 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:16:48+00:00 |
text-generation | transformers |
# zephyr_0.2_a2.5
The extrapolated (ExPO) model based on `chujiezheng/zephyr_0.2` and `alignment-handbook/zephyr-7b-sft-full`, as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper.
Specifically, we obtain this model by extrapolating from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. | {"language": ["en"], "license": "apache-2.0"} | chujiezheng/zephyr_0.2_a2.5 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"arxiv:2404.16792",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:17:53+00:00 |
null | null | {} | bingogogogo/llama3-8b-oig-unsloth-f16-GGUF | null | [
"gguf",
"region:us"
]
| null | 2024-04-28T05:19:39+00:00 |
|
image-to-image | diffusers |
# Check out more codes on our [github repository](https://github.com/yisol/IDM-VTON)!
# IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild
This is an official implementation of paper 'Improving Diffusion Models for Authentic Virtual Try-on in the Wild'
- [paper](https://arxiv.org/abs/2403.05139)
- [project page](https://idm-vton.github.io/)
๐ค Try our huggingface [Demo](https://huggingface.co/spaces/yisol/IDM-VTON)
## TODO LIST
- [x] demo model
- [x] inference code
- [ ] training code
## Acknowledgements
For the demo, GPUs are supported from [zerogpu](https://huggingface.co/zero-gpu-explorers), and auto masking generation codes are based on [OOTDiffusion](https://github.com/levihsu/OOTDiffusion) and [DCI-VTON](https://github.com/bcmi/DCI-VTON-Virtual-Try-On).
Parts of the code are based on [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter).
## Citation
```
@article{choi2024improving,
title={Improving Diffusion Models for Virtual Try-on},
author={Choi, Yisol and Kwak, Sangkyung and Lee, Kyungmin and Choi, Hyungwon and Shin, Jinwoo},
journal={arXiv preprint arXiv:2403.05139},
year={2024}
}
```
## License
The codes and checkpoints in this repository are under the [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
| {"license": "cc-by-nc-sa-4.0", "tags": ["stable-diffusion-xl", "inpainting", "virtual try-on"], "base_model": "stable-diffusion-xl-1.0-inpainting-0.1"} | imaginairy/idm-vton-safetensors | null | [
"diffusers",
"onnx",
"stable-diffusion-xl",
"inpainting",
"virtual try-on",
"arxiv:2403.05139",
"base_model:stable-diffusion-xl-1.0-inpainting-0.1",
"license:cc-by-nc-sa-4.0",
"diffusers:StableDiffusionXLInpaintPipeline",
"region:us"
]
| null | 2024-04-28T05:20:12+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 ๐ค 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": []} | OwOOwO/llamafinal2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:20:28+00:00 |
question-answering | transformers | {} | amroadel1/ltgbert-qa | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"custom_code",
"region:us"
]
| null | 2024-04-28T05:21:58+00:00 |
|
text-generation | transformers |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]} | HemaCh/gorillafinetuned | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:22:16+00:00 |
null | null | {} | CenturionHeart/DOLL | null | [
"region:us"
]
| null | 2024-04-28T05:22:44+00:00 |
|
automatic-speech-recognition | transformers |
This is a working space for a fine tune of Distil-Whisper-Large for medical speech recognition. The model will change often, so duplicate the space if you find it useful for your needs as it is.
# Distil-Whisper: distil-large-v3
Distil-Whisper was proposed in the paper [Robust Knowledge Distillation via Large-Scale Pseudo Labelling](https://arxiv.org/abs/2311.00430).
This is the third and final installment of the Distil-Whisper English series. It the knowledge distilled version of
OpenAI's [Whisper large-v3](https://huggingface.co/openai/whisper-large-v3), the latest and most performant Whisper model
to date.
Compared to previous Distil-Whisper models, the distillation procedure for distil-large-v3 has been adapted to give
**superior long-form transcription accuracy** with OpenAI's **sequential long-form algorithm**.
The result is a distilled model that performs to within 1% WER of large-v3 on long-form audio using both the sequential
and chunked algorithms, and outperforms distil-large-v2 by 4.8% using the sequential algorithm. The model is also faster
than previous Distil-Whisper models: **6.3x faster than large-v3**, and 1.1x faster than distil-large-v2.
| Model | Params / M | Rel. Latency | Short-Form | Sequential Long-Form | Chunked Long-Form |
|------------------------------------------------------------------------------|------------|--------------|------------|----------------------|-------------------|
| [large-v3](https://huggingface.co/openai/whisper-large-v3) | 1550 | 1.0 | 8.4 | 10.0 | 11.0 |
| **[distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3)** | **756** | **6.3** | **9.7** | **10.8** | **10.9** |
| [distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2) | 756 | 5.8 | 10.1 | 15.6 | 11.6 |
Since the sequential algorithm is the "de-facto" transcription algorithm across the most popular Whisper libraries
(Whisper cpp, Faster-Whisper, OpenAI Whisper), this distilled model is designed to be compatible with these libraries.
You can expect significant performance gains by switching from previous Distil-Whisper checkpoints to distil-large-v3
when using these libraries. For convenience, the weights for the most popular libraries are already converted,
with instructions for getting started below.
## Table of Contents
1. [Transformers Usage](#transformers-usage)
* [Short-Form Transcription](#short-form-transcription)
* [Sequential Long-Form](#sequential-long-form)
* [Chunked Long-Form](#chunked-long-form)
* [Speculative Decoding](#speculative-decoding)
* [Additional Speed and Memory Improvements](#additional-speed--memory-improvements)
2. [Library Integrations](#library-integrations)
* [Whisper cpp](#whispercpp)
* [Faster Whisper](#faster-whisper)
* [OpenAI Whisper](#openai-whisper)
* [Transformers.js](#transformersjs)
* [Candle](#candle)
3. [Model Details](#model-details)
4. [License](#license)
## Transformers Usage
distil-large-v3 is supported in the Hugging Face ๐ค Transformers library from version 4.39 onwards. To run the model, first
install the latest version of Transformers. For this example, we'll also install ๐ค Datasets to load a toy audio dataset
from the Hugging Face Hub:
```bash
pip install --upgrade pip
pip install --upgrade transformers accelerate datasets[audio]
```
### Short-Form Transcription
The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
class to transcribe short-form audio files (< 30-seconds) as follows:
```python
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from datasets import load_dataset
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "distil-whisper/distil-large-v3"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
torch_dtype=torch_dtype,
device=device,
)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
sample = dataset[0]["audio"]
result = pipe(sample)
print(result["text"])
```
To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline:
```diff
- result = pipe(sample)
+ result = pipe("audio.mp3")
```
For segment-level timestamps, pass the argument `return_timestamps=True` and return the `"chunks"` output:
```python
result = pipe(sample, return_timestamps=True)
print(result["chunks"])
```
<details>
<summary> For more control over the generation parameters, use the model + processor API directly: </summary>
Ad-hoc generation arguments can be passed to `model.generate`, including `num_beams` for beam-search, `return_timestamps`
for segment-level timestamps, and `prompt_ids` for prompting. See the [docstrings](https://huggingface.co/docs/transformers/en/model_doc/whisper#transformers.WhisperForConditionalGeneration.generate)
for more details.
```python
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
from datasets import Audio, load_dataset
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "distil-whisper/distil-large-v3"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
dataset = dataset.cast_column("audio", Audio(processor.feature_extractor.sampling_rate))
sample = dataset[0]["audio"]
input_features = processor(
sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt"
).input_features
input_features = input_features.to(device, dtype=torch_dtype)
gen_kwargs = {
"max_new_tokens": 128,
"num_beams": 1,
"return_timestamps": False,
}
pred_ids = model.generate(input_features, **gen_kwargs)
pred_text = processor.batch_decode(pred_ids, skip_special_tokens=True, decode_with_timestamps=gen_kwargs["return_timestamps"])
print(pred_text)
```
</details>
### Sequential Long-Form
Unlike previous Distil-Whisper releases, distil-large-v3 is specifically designed to be compatible with OpenAI's sequential
long-form transcription algorithm. This algorithm uses a sliding window for buffered inference of long audio files (> 30-seconds),
and returns more accurate transcriptions compared to the [chunked long-form algorithm](#chunked-long-form).
The sequential long-form algorithm should be used in either of the following scenarios:
1. Transcription accuracy is the most important factor, and latency is less of a consideration
2. You are transcribing **batches** of long audio files, in which case the latency of sequential is comparable to chunked, while being up to 0.5% WER more accurate
If you are transcribing single long audio files and latency is the most important factor, you should use the chunked algorithm
described [below](#chunked-long-form). For a detailed explanation of the different algorithms, refer to Sections 5 of
the [Distil-Whisper paper](https://arxiv.org/pdf/2311.00430.pdf).
The [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
class can be used to transcribe long audio files with the sequential algorithm as follows:
```python
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from datasets import load_dataset
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "distil-whisper/distil-large-v3"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
torch_dtype=torch_dtype,
device=device,
)
dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
sample = dataset[0]["audio"]
result = pipe(sample)
print(result["text"])
```
<details>
<summary> For more control over the generation parameters, use the model + processor API directly: </summary>
```python
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
from datasets import Audio, load_dataset
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "distil-whisper/distil-large-v3"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
dataset = dataset.cast_column("audio", Audio(processor.feature_extractor.sampling_rate))
sample = dataset[0]["audio"]
inputs = processor(
sample["array"],
sampling_rate=sample["sampling_rate"],
return_tensors="pt",
truncation=False,
padding="longest",
return_attention_mask=True,
)
inputs = inputs.to(device, dtype=torch_dtype)
gen_kwargs = {
"max_new_tokens": 448,
"num_beams": 1,
"condition_on_prev_tokens": False,
"compression_ratio_threshold": 1.35, # zlib compression ratio threshold (in token space)
"temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
"logprob_threshold": -1.0,
"no_speech_threshold": 0.6,
"return_timestamps": True,
}
pred_ids = model.generate(**i nputs, **gen_kwargs)
pred_text = processor.batch_decode(pred_ids, skip_special_tokens=True, decode_with_timestamps=False)
print(pred_text)
```
</details>
### Chunked Long-Form
distil-large-v3 remains compatible with the Transformers chunked long-form algorithm. This algorithm should be used when
a single large audio file is being transcribed and the fastest possible inference is required. In such circumstances,
the chunked algorithm is up to 9x faster than OpenAI's sequential long-form implementation (see Table 7 of the
[Distil-Whisper paper](https://arxiv.org/pdf/2311.00430.pdf)).
To enable chunking, pass the `chunk_length_s` parameter to the `pipeline`. For distil-large-v3, a chunk length of 25-seconds
is optimal. To activate batching over long audio files, pass the argument `batch_size`:
```python
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from datasets import load_dataset
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "distil-whisper/distil-large-v3"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
chunk_length_s=25,
batch_size=16,
torch_dtype=torch_dtype,
device=device,
)
dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
sample = dataset[0]["audio"]
result = pipe(sample)
print(result["text"])
```
### Speculative Decoding
distil-large-v3 is the first Distil-Whisper model that can be used as an assistant to Whisper large-v3 for [speculative decoding](https://huggingface.co/blog/whisper-speculative-decoding).
Speculative decoding mathematically ensures that exactly the same outputs as Whisper are obtained, while being 2 times faster.
This makes it the perfect drop-in replacement for existing Whisper pipelines, since the same outputs are guaranteed.
In the following code-snippet, we load the assistant Distil-Whisper model standalone to the main Whisper pipeline. We then
specify it as the "assistant model" for generation:
```python
from transformers import pipeline, AutoModelForCausalLM, AutoModelForSpeechSeq2Seq, AutoProcessor
import torch
from datasets import load_dataset
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
assistant_model_id = "distil-whisper/distil-large-v3"
assistant_model = AutoModelForCausalLM.from_pretrained(
assistant_model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
assistant_model.to(device)
model_id = "openai/whisper-large-v3"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
generate_kwargs={"assistant_model": assistant_model},
torch_dtype=torch_dtype,
device=device,
)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
sample = dataset[0]["audio"]
result = pipe(sample)
print(result["text"])
```
For more details on speculative decoding, refer to the blog post [Speculative Decoding for 2x Faster Whisper Inference](https://huggingface.co/blog/whisper-speculative-decoding).
### Additional Speed & Memory Improvements
You can apply additional speed and memory improvements to Distil-Whisper to further reduce the inference speed and VRAM
requirements. These optimisations primarily target the attention kernel, swapping it from an eager implementation to a
more efficient flash attention version.
#### Flash Attention 2
We recommend using [Flash-Attention 2](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#flashattention-2)
if your GPU allows for it. To do so, you first need to install [Flash Attention](https://github.com/Dao-AILab/flash-attention):
```
pip install flash-attn --no-build-isolation
```
Then pass `attn_implementation="flash_attention_2"` to `from_pretrained`:
```diff
- model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)
+ model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True, attn_implementation="flash_attention_2")
```
#### Torch Scale-Product-Attention (SDPA)
If your GPU does not support Flash Attention, we recommend making use of PyTorch [scaled dot-product attention (SDPA)](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html).
This attention implementation is activated **by default** for PyTorch versions 2.1.1 or greater. To check
whether you have a compatible PyTorch version, run the following Python code snippet:
```python
from transformers.utils import is_torch_sdpa_available
print(is_torch_sdpa_available())
```
If the above returns `True`, you have a valid version of PyTorch installed and SDPA is activated by default. If it
returns `False`, you need to upgrade your PyTorch version according to the [official instructions](https://pytorch.org/get-started/locally/)
Once a valid PyTorch version is installed, SDPA is activated by default. It can also be set explicitly by specifying
`attn_implementation="sdpa"` as follows:
```diff
- model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)
+ model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True, attn_implementation="sdpa")
```
#### Torch compile
Coming soon...
#### 4-bit and 8-bit Inference
Coming soon...
## Library Integrations
### Whisper.cpp
Distil-Whisper can be run with the [Whisper.cpp](https://github.com/ggerganov/whisper.cpp) package with the original
sequential long-form transcription algorithm. In a provisional benchmark on Mac M1, distil-large-v3 is over 5x faster
than Whisper large-v3, while performing to within 0.8% WER over long-form audio.
Steps for getting started:
1. Clone the Whisper.cpp repository:
```
git clone https://github.com/ggerganov/whisper.cpp.git
cd whisper.cpp
```
2. Install the Hugging Face Hub Python package:
```bash
pip install --upgrade huggingface_hub
```
And download the GGML weights for distil-large-v3 using the following Python snippet:
```python
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id='distil-whisper/distil-large-v3-ggml', filename='ggml-distil-large-v3.bin', local_dir='./models')
```
Note that if you do not have a Python environment set-up, you can also download the weights directly with `wget`:
```bash
wget https://huggingface.co/distil-whisper/distil-large-v3-ggml/resolve/main/ggml-distil-large-v3.bin -P ./models
```
3. Run inference using the provided sample audio:
```bash
make -j && ./main -m models/ggml-distil-large-v3.bin -f samples/jfk.wav
```
### Faster-Whisper
Faster-Whisper is a reimplementation of Whisper using [CTranslate2](https://github.com/OpenNMT/CTranslate2/), a fast
inference engine for Transformer models.
First, install the Faster-Whisper package according to the [official instructions](https://github.com/SYSTRAN/faster-whisper#installation).
For this example, we'll also install ๐ค Datasets to load a toy audio dataset from the Hugging Face Hub:
```bash
pip install --upgrade pip
pip install --upgrade git+https://github.com/SYSTRAN/faster-whisper datasets[audio]
```
The following code snippet loads the distil-large-v3 model and runs inference on an example file from the LibriSpeech ASR
dataset:
```python
import torch
from faster_whisper import WhisperModel
from datasets import load_dataset
# define our torch configuration
device = "cuda:0" if torch.cuda.is_available() else "cpu"
compute_type = "float16" if torch.cuda.is_available() else "float32"
# load model on GPU if available, else cpu
model = WhisperModel("distil-large-v3", device=device, compute_type=compute_type)
# load toy dataset for example
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
sample = dataset[1]["audio"]["path"]
segments, info = model.transcribe(sample, beam_size=1)
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```
To transcribe a local audio file, simply pass the path to the audio file as the `audio` argument to transcribe:
```python
segments, info = model.transcribe("audio.mp3", beam_size=1)
```
### OpenAI Whisper
To use the model in the original Whisper format, first ensure you have the [`openai-whisper`](https://pypi.org/project/openai-whisper/) package installed.
For this example, we'll also install ๐ค Datasets to load a toy audio dataset from the Hugging Face Hub:
```bash
pip install --upgrade pip
pip install --upgrade openai-whisper datasets[audio]
```
The following code-snippet demonstrates how to transcribe a sample file from the LibriSpeech dataset loaded using
๐ค Datasets:
```python
from huggingface_hub import hf_hub_download
from datasets import load_dataset
from whisper import load_model, transcribe
model_path = hf_hub_download(repo_id="distil-whisper/distil-large-v3-openai", filename="model.bin")
model = load_model(model_path)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
sample = dataset[0]["audio"]["path"]
pred_out = transcribe(model, audio=sample, language="en")
print(pred_out["text"])
```
Note that the model weights will be downloaded and saved to your cache the first time you run the example. Subsequently,
you can re-use the same example, and the weights will be loaded directly from your cache without having to download them
again.
To transcribe a local audio file, simply pass the path to the audio file as the `audio` argument to transcribe:
```python
pred_out = transcribe(model, audio=sample, language="en")
```
The Distil-Whisper model can also be used with the OpenAI Whisper CLI. Refer to the [following instructions](https://huggingface.co/distil-whisper/distil-large-v3-openai#cli-usage)
for details.
### Transformers.js
Distil-Whisper can be run completely in your web browser with [Transformers.js](http://github.com/xenova/transformers.js):
1. Install Transformers.js from [NPM](https://www.npmjs.com/package/@xenova/transformers):
```bash
npm i @xenova/transformers
```
2. Import the library and perform inference with the pipeline API.
```js
import { pipeline } from '@xenova/transformers';
const transcriber = await pipeline('automatic-speech-recognition', 'distil-whisper/distil-large-v3');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
const output = await transcriber(url);
// { text: " And so, my fellow Americans, ask not what your country can do for you. Ask what you can do for your country." }
```
Check out the online [Distil-Whisper Web Demo](https://huggingface.co/spaces/Xenova/distil-whisper-web) to try it out yourself.
As you'll see, it runs locally in your browser: no server required!
Refer to the Transformers.js [docs](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.AutomaticSpeechRecognitionPipeline)
for further information.
### Candle
Through an integration with Hugging Face [Candle](https://github.com/huggingface/candle/tree/main) ๐ฏ๏ธ, Distil-Whisper is
available in the Rust library ๐ฆ
Benefit from:
* Optimised CPU backend with optional MKL support for Linux x86 and Accelerate for Macs
* Metal support for efficiently running on Macs
* CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL
* WASM support: run Distil-Whisper in a browser
Steps for getting started:
1. Install [`candle-core`](https://github.com/huggingface/candle/tree/main/candle-core) as explained [here](https://huggingface.github.io/candle/guide/installation.html)
2. Clone the `candle` repository locally:
```
git clone https://github.com/huggingface/candle.git
```
3. Enter the example directory for [Whisper](https://github.com/huggingface/candle/tree/main/candle-examples/examples/whisper):
```
cd candle/candle-examples/examples/whisper
```
4. Run an example:
```
cargo run --example whisper --release --features symphonia -- --model distil-large-v3
```
5. To specify your own audio file, add the `--input` flag:
```
cargo run --example whisper --release --features symphonia -- --model distil-large-v3 --input audio.wav
```
**Tip:** for compiling using Apple Metal, specify the `metal` feature when you run the example:
```
cargo run --example whisper --release --features="symphonia,metal" -- --model distil-large-v3
```
Note that if you encounter the error:
```
error: target `whisper` in package `candle-examples` requires the features: `symphonia`
Consider enabling them by passing, e.g., `--features="symphonia"`
```
You should clean your `cargo` installation:
```
cargo clean
```
And subsequently recompile:
```
cargo run --example whisper --release --features symphonia -- --model distil-large-v3
```
## Model Details
Distil-Whisper inherits the encoder-decoder architecture from Whisper. The encoder maps a sequence of speech vector
inputs to a sequence of hidden-state vectors. The decoder auto-regressively predicts text tokens, conditional on all
previous tokens and the encoder hidden-states. Consequently, the encoder is only run forward once, whereas the decoder
is run as many times as the number of tokens generated. In practice, this means the decoder accounts for over 90% of
total inference time. Thus, to optimise for latency, the focus is on minimising the inference time of the decoder.
To distill the Whisper model, we reduce the number of decoder layers while keeping the encoder fixed.
The encoder (shown in green) is entirely copied from the teacher to the student and frozen during training.
The student's decoder consists of a subset of the teacher decoder layers, which are intialised from maximally spaced layers.
The model is then trained on a weighted sum of the KL divergence and pseudo-label loss terms.
<p align="center">
<img src="https://huggingface.co/datasets/distil-whisper/figures/resolve/main/architecture.png?raw=true" width="600"/>
</p>
## Differences with distil-large-v2
Compared to previous version of Distil-Whisper, distil-large-v3 is specifically designed to target the OpenAI sequential
long-form transcription algorithm. There are no architectural differences compared to distil-large-v2, other than the fact
the model layers are intialised from the latest large-v3 model rather than the older large-v2 one. The differences lie
in the way the model was trained.
Previous Distil-Whisper models were trained on a mean input length of 7-seconds, whereas the original Whisper models were
pre-trained on 30-second inputs. During distillation, we shift the distribution of the model weights to the distribution
of our training data. If our training data contains shorter utterances (e.g. on average 7-seconds audio instead of 30-seconds),
then the predicted distribution shifts to this shorter context length. At inference time, the optimal context window for
distil-large-v2 was an interpolation of these two values: 15-seconds. Beyond this time, the predictions for the distil-large-v2
model were largely inaccurate, particularly for the timestamp predictions. However, the sequential long-form algorithm
uses 30-second sliding windows for inference, with the window shifted according to the last predicted timestamp. Since the
last timestamp typically occurs after the 15-second mark, it was predicted with low accuracy, causing the long-form
transcription to often fail.
To preserve Whisper's ability to transcribe sliding 30-second windows, as is done with sequential decoding, we need to
ensure the context length of distil-large-v3 is also 30-seconds. This was primarily achieved with four strategies:
1. **Packing the audio samples in the training dataset to 30-seconds:** since the model is both pre-trained and distilled on audio data packed to 30-seconds, distil-large-v3 now operates on the same ideal context window as Whisper, predicting accurate timestamps up to and including 30-seconds.
2. **Freezing the decoder input embeddings:** we use the same input embeds representation as the original model, which is designed to handle longer context lengths than previous Distil-Whisper iterations.
3. **Using a longer maximum context length during training:** instead of training on a maximum target length of 128, we train on a maximum of 256. This helps distil-large-v3 transcribe 30-second segments where the number of tokens possibly exceeds 128.
4. **Appending prompt conditioning to 50% of the training samples:** enables the model to be used with the `condition_on_prev_tokens` argument, and context windows up to 448 tokens.
There were further tricks that were employed to improve the performance of distil-large-v3 under the sequential decoding
algorithm, which we be explained fully in an upcoming blog post.
## Evaluation
The following code-snippets demonstrates how to evaluate the Distil-Whisper model on the LibriSpeech validation-clean
dataset with [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet), meaning no
audio data has to be downloaded to your local device.
First, we need to install the required packages, including ๐ค Datasets to stream and load the audio data, and ๐ค Evaluate to
perform the WER calculation:
```bash
pip install --upgrade pip
pip install --upgrade transformers datasets[audio] evaluate jiwer
```
Evaluation can then be run end-to-end with the following example:
```python
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
from datasets import load_dataset
from evaluate import load
import torch
from tqdm import tqdm
# define our torch configuration
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "distil-whisper/distil-large-v3"
# load the model + processor
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, use_safetensors=True, low_cpu_mem_usage=True)
model = model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
# load the dataset with streaming mode
dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
# define the evaluation metric
wer_metric = load("wer")
def inference(batch):
# 1. Pre-process the audio data to log-mel spectrogram inputs
audio = [sample["array"] for sample in batch["audio"]]
input_features = processor(audio, sampling_rate=batch["audio"][0]["sampling_rate"], return_tensors="pt").input_features
input_features = input_features.to(device, dtype=torch_dtype)
# 2. Auto-regressively generate the predicted token ids
pred_ids = model.generate(input_features, max_new_tokens=128)
# 3. Decode the token ids to the final transcription
batch["transcription"] = processor.batch_decode(pred_ids, skip_special_tokens=True)
batch["reference"] = batch["text"]
return batch
# batch size 16 inference
dataset = dataset.map(function=inference, batched=True, batch_size=16)
all_transcriptions = []
all_references = []
# iterate over the dataset and run inference
for result in tqdm(dataset, desc="Evaluating..."):
all_transcriptions.append(result["transcription"])
all_references.append(result["reference"])
# normalize predictions and references
all_transcriptions = [processor.normalize(transcription) for transcription in all_transcriptions]
all_references = [processor.normalize(reference) for reference in all_references]
# compute the WER metric
wer = 100 * wer_metric.compute(predictions=all_transcriptions, references=all_references)
print(wer)
```
**Print Output:**
```
2.428920763531516
```
## Intended Use
Distil-Whisper is intended to be a drop-in replacement for Whisper large-v3 on English speech recognition. In particular, it
achieves comparable WER results over out-of-distribution (OOD) test data, while being 6x faster on both short and long-form audio.
## Data
Distil-Whisper is trained on 22,000 hours of audio data from nine open-source, permissively licensed speech datasets on the
Hugging Face Hub:
| Dataset | Size / h | Speakers | Domain | Licence |
|-----------------------------------------------------------------------------------------|----------|----------|-----------------------------|-----------------|
| [People's Speech](https://huggingface.co/datasets/MLCommons/peoples_speech) | 12,000 | unknown | Internet Archive | CC-BY-SA-4.0 |
| [Common Voice 13](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) | 3,000 | unknown | Narrated Wikipedia | CC0-1.0 |
| [GigaSpeech](https://huggingface.co/datasets/speechcolab/gigaspeech) | 2,500 | unknown | Audiobook, podcast, YouTube | apache-2.0 |
| Fisher | 1,960 | 11,900 | Telephone conversations | LDC |
| [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) | 960 | 2,480 | Audiobooks | CC-BY-4.0 |
| [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) | 540 | 1,310 | European Parliament | CC0 |
| [TED-LIUM](https://huggingface.co/datasets/LIUM/tedlium) | 450 | 2,030 | TED talks | CC-BY-NC-ND 3.0 |
| SwitchBoard | 260 | 540 | Telephone conversations | LDC |
| [AMI](https://huggingface.co/datasets/edinburghcstr/ami) | 100 | unknown | Meetings | CC-BY-4.0 |
||||||
| **Total** | 21,770 | 18,260+ | | |
The combined dataset spans 10 distinct domains and over 50k speakers. The diversity of this dataset is crucial to ensuring
the distilled model is robust to audio distributions and noise.
The audio data is then pseudo-labelled using the Whisper large-v3 model: we use Whisper to generate predictions for all
the audio in our training set and use these as the target labels during training. Using pseudo-labels ensures that the
transcriptions are consistently formatted across datasets and provides sequence-level distillation signal during training.
## WER Filter
The Whisper pseudo-label predictions are subject to mis-transcriptions and hallucinations. To ensure we only train on
accurate pseudo-labels, we employ a simple WER heuristic during training. First, we normalise the Whisper pseudo-labels
and the ground truth labels provided by each dataset. We then compute the WER between these labels. If the WER exceeds
a specified threshold, we discard the training example. Otherwise, we keep it for training.
Section 9.2 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430) demonstrates the effectiveness of this filter
for improving downstream performance of the distilled model. We also partially attribute Distil-Whisper's robustness to
hallucinations to this filter.
## Training
The model was trained for 80,000 optimisation steps (or 11 epochs) with batch size 256. The Tensorboard training logs can
be found under: https://huggingface.co/distil-whisper/distil-large-v3/tensorboard?params=scalars#frame
## Results
The distilled model performs to within 1.5% WER of Whisper large-v3 on out-of-distribution (OOD) short-form audio, within
1% WER on sequential long-form decoding, and outperforms large-v3 by 0.1% on chunked long-form. This performance gain is
attributed to lower hallucinations.
For a detailed per-dataset breakdown of the evaluation results, refer to Tables 16 and 17 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430)
Distil-Whisper is also evaluated on the [ESB benchmark](https://arxiv.org/abs/2210.13352) datasets as part of the [OpenASR leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard),
where it performs to within 0.2% WER of Whisper.
## Reproducing Distil-Whisper
Training and evaluation code to reproduce Distil-Whisper is available under the Distil-Whisper repository: https://github.com/huggingface/distil-whisper/tree/main/training
This code will shortly be updated to include the training updates described in the section [Differences with distil-large-v2](#differences-with-distil-large-v2).
## License
Distil-Whisper inherits the [MIT license](https://github.com/huggingface/distil-whisper/blob/main/LICENSE) from OpenAI's Whisper model.
## Citation
If you use this model, please consider citing the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430):
```
@misc{gandhi2023distilwhisper,
title={Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling},
author={Sanchit Gandhi and Patrick von Platen and Alexander M. Rush},
year={2023},
eprint={2311.00430},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Acknowledgements
* OpenAI for the Whisper [model](https://huggingface.co/openai/whisper-large-v3), in particular Jong Wook Kim for the [original codebase](https://github.com/openai/whisper) and training discussions
* Hugging Face ๐ค [Transformers](https://github.com/huggingface/transformers) for the model integration
* [Georgi Gerganov](https://huggingface.co/ggerganov) for the Whisper cpp integration
* [Systran team](https://github.com/SYSTRAN) for the Faster-Whisper integration
* [Joshua Lochner](https://huggingface.co/xenova) for the Transformers.js integration
* [Laurent Mazare](https://huggingface.co/lmz) for the Candle integration
* [Vaibhav Srivastav](https://huggingface.co/reach-vb) for Distil-Whisper distribution
* Google's [TPU Research Cloud (TRC)](https://sites.research.google/trc/about/) programme for Cloud TPU v4 compute resource
* [Raghav Sonavane](https://huggingface.co/rsonavane/distil-whisper-large-v2-8-ls) for an early iteration of Distil-Whisper on the LibriSpeech dataset | {"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["audio", "automatic-speech-recognition", "transformers.js"], "widget": [{"example_title": "LibriSpeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "LibriSpeech sample 2", "src": "https://cdn-media.huggingface.co/speech_samples/sample2.flac"}], "pipeline_tag": "automatic-speech-recognition"} | Crystalcareai/Whisper-Medicalv1 | null | [
"transformers",
"jax",
"tensorboard",
"onnx",
"safetensors",
"whisper",
"automatic-speech-recognition",
"audio",
"transformers.js",
"en",
"arxiv:2311.00430",
"arxiv:2210.13352",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:23:22+00:00 |
text-generation | transformers |
# zephyr-7b-beta-ExPO
The extrapolated (ExPO) model based on `HuggingFaceH4/zephyr-7b-beta` and `HuggingFaceH4/mistral-7b-sft-beta`, as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" paper.
Specifically, we obtain this model by extrapolating from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference. | {"language": ["en"], "license": "apache-2.0"} | chujiezheng/zephyr-7b-beta-ExPO | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"arxiv:2404.16792",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:23:38+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/wvza3br | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:23:41+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/ck0nwso | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:23:46+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/5bswem6 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:23:50+00:00 |
null | null | {"license": "apache-2.0"} | Frank999/Octopus-v2-q4f16_1-MLC | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-28T05:24:29+00:00 |
|
null | null | {"license": "openrail"} | GoldoBasic/babybotgpt | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-28T05:26:47+00:00 |
|
null | null |
# Model Card for Emu
Some alignments in these domains:
- Bitcoin
- Nostr
- Health
- Permaculture
- Phytochemicals
- Alternative medicine
- Herbs
- Nutrition
I am having success with chat template of Llama3: \<\|begin_of_text\|\>\<\|start_header_id\|\> ...
You can check the GGUF chat template to see the exact format. But I didn't change it, so Llama3 format continues.
GGUF has the necessary eot token to properly stop.
## Model Details
- **Fine tuned by:** someone
- **Finetuned from model:** https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct
## Uses
Ask any question, compared to other models this may know more about those topics above.
You can use llama.cpp to chat with it.
You can also use llama-cpp-python package to chat with it in a Python script.
This is how you generate prompt and stops:
```
prompt = f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{sys_msg}<|eot_id|>"
i = 0
while i < len(msgs):
prompt += f"<|start_header_id|>user<|end_header_id|>\n\n{msgs[i]['content']}<|eot_id|>"
prompt += f"<|start_header_id|>assistant<|end_header_id|>\n\n{msgs[i + 1]['content']}<|eot_id|>"
i += 2
prompt += f"<|start_header_id|>user<|end_header_id|>\n\n{q}<|eot_id|>"
prompt += "<|start_header_id|>assistant<|end_header_id|>\n\n"
stops = ['<|eot_id|>', '<|end_of_text|>', '<|im_end|>', '<|start_header_id|>']
```
## Warning
Users (both direct and downstream) should be aware of the risks, biases and limitations of the model.
The trainer, developer or uploader of this model does not assume any liability. Use it at your own risk.
## Training Details
### Training Data
Some data I curated from various sources.
### Training Procedure
LLaMa-Factory is used to train on 2x3090!
fsdp_qlora is the technique.
| {"license": "apache-2.0"} | some1nostr/Emu-70B-Llama3 | null | [
"gguf",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-28T05:29:09+00:00 |
null | null | {"license": "bigscience-bloom-rail-1.0"} | Sisterfoot1/Cover | null | [
"license:bigscience-bloom-rail-1.0",
"region:us"
]
| null | 2024-04-28T05:31:15+00:00 |
|
text-generation | transformers |
# PolyLM-13b-WangchanX-sft-Demo
Built with PolyLM-13b (Fine tuning with Qlora)
This model is based on [WangchanX Fine-tuning Pipeline](https://github.com/vistec-AI/WangchanX).
GitHub: [WangchanX Fine-tuning Pipeline](https://github.com/vistec-AI/WangchanX).
License: cc-by-nc-3.0
## Train Example
Train WangchanX pipeline: [Colab](https://colab.research.google.com/github/vistec-AI/WangchanX/blob/main/notebooks/Train_WangchanX_pipeline.ipynb)
## Inference Example
Run on [Colab](https://colab.research.google.com/drive/1PeUnv89Ao2uHRYYzZVOlUwoBUdYKFbLS?usp=sharing)
### Prepare your model and tokenizer:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Model path
path = "airesearch/PolyLM-13b-WangchanX-sft-Demo"
# Device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(path, device_map="auto")
```
### Define chat messages:
```python
messages = [
{"role": "user", "content": "เธฃเธนเนเธเธฑเธเธเธฃเธฐเนเธเธจเนเธเธขเนเธซเธก"},
]
```
### Tokenize chat messages:
```python
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(device)
print(tokenizer.decode(tokenized_chat[0]))
```
<details close>
<summary>Output: </summary>
<br>
<pre lang="markdown">
<|user|>
เธฃเธนเนเธเธฑเธเธเธฃเธฐเนเธเธจเนเธเธขเนเธซเธก</s>
<|assistant|></pre>
</details>
### Generate responses:
```python
outputs = model.generate(tokenized_chat, max_length=2048)
print(tokenizer.decode(outputs[0]))
```
<details close>
<summary>Output: </summary>
<br>
<pre lang="markdown">
<|user|>
เธฃเธนเนเธเธฑเธเธเธฃเธฐเนเธเธจเนเธเธขเนเธซเธก</s>
<|assistant|>
เธเธญเนเธเธฐเธเธณเนเธซเนเธเธฃเธฒเธเธงเนเธฒ เธเธฃเธฐเนเธเธจเนเธเธขเนเธเนเธเธเธดเธเนเธเธเธเธตเนเธกเธตเธเธงเธฒเธกเธซเธฅเธฒเธเธซเธฅเธฒเธขเธเธฒเธเธงเธฑเธเธเธเธฃเธฃเธกเนเธฅเธฐเธกเธตเธเธฃเธฐเนเธเธเธตเธเธตเนเธเธเธเธฒเธกเธกเธฒเธเธกเธฒเธข เธกเธตเธญเธฒเธฃเธขเธฐเธเธฃเธฃเธกเนเธเธญเธเธตเธเธเธตเนเธกเธตเธกเธฒเธเธฑเนเธเนเธเนเธชเธกเธฑเธขเธเนเธญเธเธเธธเธเธเธเธฒเธฅ เนเธเนเธเธจเธนเธเธขเนเธเธฅเธฒเธเธเธญเธเธเธฒเธงเนเธเธฒเนเธเนเธฒเธเนเธฒเธเน เนเธเนเธญเนเธเธตเธข เนเธฅเธฐเธขเธฑเธเธเธเธชเธ เธฒเธเธเธงเธฒเธกเธฃเธธเนเธเนเธฃเธเธเนเนเธเนเธญเธขเนเธฒเธเนเธกเนเธเนเธฒเนเธเธทเนเธญเธเนเธงเธขเธเธฒเธฃเธเธฑเธเธเธฒเนเธจเธฃเธฉเธเธเธดเธเธเธตเนเนเธเธดเธเนเธเธฃเธงเธเนเธฃเนเธงเธเธตเนเธชเธธเธเนเธเธ เธนเธกเธดเธ เธฒเธเธเธตเน เธเธญเธเธเธฒเธเธเธฑเนเธเนเธฅเนเธงเธเนเธขเธฑเธเนเธเนเธฃเธฑเธเนเธฅเธทเธญเธเนเธซเนเธเธฑเธเธเธฒเธฃเนเธเนเธเธเธฑเธเธเธตเธฌเธฒเธเธตเนเธเธกเธชเนเธเธฃเธฑเนเธเธฅเนเธฒเธชเธธเธเนเธกเธทเนเธญเธเธต เธ.เธจ.2560 เธเธถเนเธเธเธทเธญเนเธเนเธเนเธซเธฃเธตเธขเธเนเธเธดเธเนเธเธเธฒเธฃเนเธเนเธเธเธฑเธเธฃเธฐเธเธฑเธเธญเธทเนเธเธญเธตเธเธเนเธงเธข เนเธเธขเธเธฑเธเธเธธเธเธฑเธเธเธตเนเธกเธตเธเธฒเธฃเธฅเธเธเธฃเธฐเธเธฒเธกเธเธดเนเธเธทเนเธญเธฃเธฑเธเธฃเธญเธเธเธฒเธฃเนเธเนเธฒเธฃเนเธงเธกเธชเธซเธเธฃเธฐเธเธฒเธเธฒเธเธดเธเนเธฒเธเธกเธเธธเธฉเธขเธเธ (ICCPR) เนเธเธทเนเธญเธเธฐเธเธณเนเธซเนเธชเธฒเธกเธฒเธฃเธเนเธเนเธชเธดเธเธเธดเธกเธเธธเธฉเธขเธเธเธเธฒเธกเธญเธเธธเธชเธฑเธเธเธฒเธชเธซเธเธฃเธฐเธเธฒเธเธฒเธเธดเนเธเธตเนเธขเธงเธเธฑเธเธชเธดเธเธเธดเธกเธเธธเธฉเธขเธเธเธเธซเธฃเธทเธญ ICCPR เนเธเนเนเธเนเธกเธญเธฑเธเธฃเธฒเธชเนเธงเธเนเธเนเธฒเธเธฑเธเธเธฑเธเธเธฑเธเธจเธถเธเธฉเธฒเธเธฑเนเธงเนเธ เนเธเนเนเธเธทเนเธญเธเธเธฒเธเธเธนเนเธเธเธเธณเธเธงเธเธเนเธญเธขเธเธงเนเธฒ เธเธถเธเนเธกเนเธกเธตเธเธฅเธเนเธญเธเธฒเธฃเธชเธกเธฑเธเธฃเนเธฃเธตเธขเธ เธเธฃเธน เธซเธฃเธทเธญเนเธกเนเธเธฃเธฐเธเธฑเนเธเธเธธเธเธเธฃเธนเนเธญเธเธเนเธญเธเธขเธญเธกเนเธซเธเธทเนเธญเธขเธเธฒเธขเนเธเธเธณเธเธฒเธเนเธเธดเนเธกเธเธถเนเธเนเธเธฃเธฒเธฐเนเธเนเธเนเธฅเนเธเธเธณเธเธงเธเธซเธเธถเนเธเนเธเธดเธเนเธซเธกเนเธเธธเธเธงเธฑเธเนเธเธขเนเธเธเธฒเธฐเธเนเธงเธเธเธตเนเนเธเธดเธเนเธเธญเธกเนเธซเธเน เธญเธขเธฒเธเธเธถเธเธเธฒเธเธเธณเธงเธดเธเธขเธฒเธฅเธฑเธขเธเธตเนเธชเธญเธเนเธฅเธขเธเนเธฐ เธเธญเธเธเธฃเธฐเธเธธเธเธเธฃเธฑเธ</s></pre>
</details> | {"language": ["th", "en"], "license": "cc-by-nc-3.0", "datasets": ["airesearch/concat_six_dataset_th_en"]} | airesearch/PolyLM-13b-WangchanX-sft-Demo | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"th",
"en",
"dataset:airesearch/concat_six_dataset_th_en",
"license:cc-by-nc-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:33:14+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 ๐ค 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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- **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]
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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]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/bba6lyr | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:34:27+00:00 |
null | null |
# OpenHermes 2.5 - Mixtral 8x22B
Mixtral 8x22B full SFTed on OpenHermes 2.5 dataset (https://huggingface.co/datasets/teknium/OpenHermes-2.5).
Evaluations are still being ran. Download the model from branches 4th-epoch and 3rd-epoch.
Prompt format is ChatML. Refer to https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B for examples.
Research supported by Google's TPU Research Cloud. | {"language": ["en"], "license": "apache-2.0", "tags": ["Mixtral", "instruct", "finetune", "chatml", "gpt4", "synthetic data", "distillation"], "datasets": ["teknium/OpenHermes-2.5"], "base_model": "mistralai/Mixtral-8x22B-v0.1"} | a-normal-username/Mixtral-8x22B-OpenHermes-2.5 | null | [
"Mixtral",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"en",
"dataset:teknium/OpenHermes-2.5",
"base_model:mistralai/Mixtral-8x22B-v0.1",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-28T05:34:32+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 ๐ค 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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | happylayers/sc69 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:34:56+00:00 |
null | peft |
# gemma-dolly-agriculture
This model is based on [google/gemma-2b](https://huggingface.co/google/gemma-2b), fine tuned with the dolly-qa dataset and some specific examples of agricultural disease descriptions.
It achieves the following results on the evaluation set:
- Loss: 2.0198
## How to Run Inference
```
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_id = "google/gemma-2b"
peft_model_id = "apfurman/gemma-dolly-agriculture"
# make sure you have access to gemma-2b as well
model = AutoModelForCausalLM.from_pretrained(model_id, token="YOUR_TOKEN_HERE")
model.load_adapter(peft_model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id, token="YOUR_TOKEN_HERE")
def ask(prompt):
inputs = tokenizer(prompt, return_tensors="pt").input_ids
with torch.inference_mode():
tokens = model.generate(
inputs,
pad_token_id=128001,
eos_token_id=128001,
max_new_tokens=200,
repetition_penalty=1.5,
)
return tokenizer.decode(tokens[0], skip_special_tokens=True)
```
## Intended uses & limitations
Created for prompting an AI about agricultural info, but more fine-tuning is needed as current results are not great.
## Training and evaluation data
## Training procedure
Trained on Intel Data Center GPU Max Series with Intel Developer Cloud running a jupyter notebook.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 1480
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 2.918 | 1.6393 | 100 | 2.5702 |
| 2.4342 | 3.2787 | 200 | 2.2747 |
| 2.2482 | 4.9180 | 300 | 2.1601 |
| 2.1554 | 6.5574 | 400 | 2.0971 |
| 2.1022 | 8.1967 | 500 | 2.0698 |
| 2.0806 | 9.8361 | 600 | 2.0544 |
| 2.0651 | 11.4754 | 700 | 2.0437 |
| 2.0439 | 13.1148 | 800 | 2.0359 |
| 2.0369 | 14.7541 | 900 | 2.0302 |
| 2.034 | 16.3934 | 1000 | 2.0263 |
| 2.0249 | 18.0328 | 1100 | 2.0236 |
| 2.0174 | 19.6721 | 1200 | 2.0218 |
| 2.0154 | 21.3115 | 1300 | 2.0203 |
| 2.0145 | 22.9508 | 1400 | 2.0198 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.0.post0+cxx11.abi
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "gemma", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "google/gemma-2b", "model-index": [{"name": "gemma-dolly-agriculture", "results": []}]} | apfurman/gemma-dolly-agriculture | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
]
| null | 2024-04-28T05:35:28+00:00 |
text-generation | transformers | # 0428
This model is a fine-tuned version of [../../models/Qwen1.5-7B-sft-0425](https://huggingface.co/../../models/Qwen1.5-7B-sft-0425) on the alpaca_formatted_review_new_data_greater_7 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0733
## Model description
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
* 8 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B and 72B dense models, and an MoE model of 14B with 2.7B activated;
* Significant performance improvement in Chat models;
* Multilingual support of both base and chat models;
* Stable support of 32K context length for models of all sizes
* No need of `trust_remote_code`.
For more details, please refer to the [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
## 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: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
| :-----------: | :---: | :--: | :-------------: |
| 0.8554 | 0.25 | 10 | 1.1541 |
| 0.6139 | 0.5 | 20 | 1.1258 |
| 0.629 | 0.75 | 30 | 1.1057 |
| 0.7943 | 1.0 | 40 | 1.0993 |
| 0.6658 | 1.25 | 50 | 1.0964 |
| 0.778 | 1.5 | 60 | 1.0892 |
| 0.593 | 1.75 | 70 | 1.0868 |
| 0.8847 | 2.0 | 80 | 1.0816 |
| 0.5067 | 2.25 | 90 | 1.0806 |
| 0.9706 | 2.5 | 100 | 1.0789 |
| 0.7302 | 2.75 | 110 | 1.0763 |
| 0.6855 | 3.0 | 120 | 1.0768 |
| 0.4358 | 3.25 | 130 | 1.0754 |
| 0.5777 | 3.5 | 140 | 1.0740 |
| 0.5687 | 3.75 | 150 | 1.0732 |
| 0.6462 | 4.0 | 160 | 1.0732 |
| 0.5465 | 4.25 | 170 | 1.0733 |
| 0.7926 | 4.5 | 180 | 1.0737 |
| 0.4968 | 4.75 | 190 | 1.0735 |
| 0.6406 | 5.0 | 200 | 1.0733 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1 | {"license": "mit"} | WDong/7B-0428 | null | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:36:00+00:00 |
null | null | {"license": "openrail"} | KeroroK66/Roboko | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-28T05:40:48+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 ๐ค 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": []} | OmAlve/roberta-finetuned-imdb-sentiment | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:41:01+00:00 |
null | null | {} | yjwon/ultrabin_ogd | null | [
"region:us"
]
| null | 2024-04-28T05:41:38+00:00 |
|
null | null | {"license": "openrail"} | KeroroK66/Iroha | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-28T05:41:49+00:00 |
|
null | null | Korean actor Park Eun-bin (Strange Lawyer Woo Young-woo) version
ํ๊ตญ ๋ฐฐ์ฐ ๋ฐ์๋น(์ด์ํ ๋ณํธ์ฌ ์ฐ์์ฐ) ๋ฒ์ | {"license": "openrail"} | YangPa/wYUNGw | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-28T05:41:58+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** gromoboy
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "gguf"], "base_model": "unsloth/gemma-2b-bnb-4bit"} | gromoboy/gemma_gguf | null | [
"transformers",
"gguf",
"gemma",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/gemma-2b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:42:00+00:00 |
null | null | {} | jimjakdiend/content | null | [
"region:us"
]
| null | 2024-04-28T05:43:15+00:00 |
|
null | null | {"license": "openrail"} | KeroroK66/HakuiKoyori | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-28T05:43:30+00:00 |
|
automatic-speech-recognition | peft | {"language": ["ja"], "license": "apache-2.0", "library_name": "peft", "tags": ["whisper", "lora", "4-bit"], "datasets": ["mozilla-foundation/common_voice_16_1", "google/fleurs"], "metrics": ["wer"], "base_model": "openai/whisper-large-v2", "pipeline_tag": "automatic-speech-recognition"} | sin2piusc/whisper-5K-adamw-adafactor-jp | null | [
"peft",
"tensorboard",
"safetensors",
"whisper",
"lora",
"4-bit",
"automatic-speech-recognition",
"ja",
"dataset:mozilla-foundation/common_voice_16_1",
"dataset:google/fleurs",
"base_model:openai/whisper-large-v2",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-28T05:44:33+00:00 |
|
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 1.0112701654434204
f1_macro: 0.23304414883362254
f1_micro: 0.5374355326338254
f1_weighted: 0.37573861876678316
precision_macro: 0.17914517754460846
precision_micro: 0.5374355326338254
precision_weighted: 0.28883695173740354
recall_macro: 0.3333333333333333
recall_micro: 0.5374355326338254
recall_weighted: 0.5374355326338254
accuracy: 0.5374355326338254
| {"tags": ["autotrain", "text-classification"], "datasets": ["autotrain-9yyoi-z5w7f/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]} | Sathvik6323/cardiffnlp-twitter-roberta-base-sentiment | null | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"autotrain",
"dataset:autotrain-9yyoi-z5w7f/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:44:40+00:00 |
text-generation | transformers | {} | yuijla/llama-2-7b-miniguanaco | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:45:55+00:00 |
|
null | null | # RWKV-x060-Japanese-11.2B
## RWKV Architecture "Finch" based 11.2B Parameters Model.
็ถ็ถใใฌใผใใณใฐไธญใงใใๅฎ้จใชใฎใงๆง่ฝ่ฉไพกใฏใใฆใใพใใใ
- "YORINOBU"
- Based on RWKV6-World v2.1 7b 53% Model, we have applied a layer expansion approach and tuned it as a 48-layer, 4096-dimensional model.
- I added 8 layers to the 40-layer model, froze layers 0 to 39, and continued pre-training layers 40 to 47, along with the Embedding and Head layers, using a Japanese corpus.
- Since it is an experimental approach, it may exhibit unpredictable behavior.
- RWKV6-World v2.1 7b 53% Modelใใใผในใซใใฌใคใคใผๆกๅผตใขใใญใผใใ้ฉ็จใใ48ๅฑค4096ๆฌกๅ
ใขใใซใจใใฆใใฅใผใใณใฐใใพใใใ
- 40ๅฑคใขใใซใซ8ๅฑคใ่ฟฝๅ ใใ0ใใ39ใฌใคใคใผใพใงใๅ็ตใใ40ใใ47ใEmbใHeadๅฑคใๆฅๆฌ่ชใณใผใในใง็ถ็ถไบๅๅญฆ็ฟใ่กใใพใใใ
- ๅฎ้จ็ใขใใญใผใใชใฎใงใไบๆธฌไธๅฏ่ฝใชๆๅใใใๅฏ่ฝๆงใใใใพใ
## Training
- using RWKV-LM-LISA Anarchy mode, Continuous Pre-traning
- https://github.com/OpenMOSE/RWKV-LM-LISA
- Single A6000 LISA 4layer training each step
2024 OpenMOSE | {"language": ["ja"], "license": "apache-2.0"} | OpenMOSE/RWKV-x060-Japanese-11.2B | null | [
"ja",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-28T05:45:57+00:00 |
text-generation | transformers | {} | anyisalin/lzlv_70b_fp16_hf-FP8-D | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:46:12+00:00 |
|
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 1.0093501806259155
f1_macro: 0.23304414883362254
f1_micro: 0.5374355326338254
f1_weighted: 0.37573861876678316
precision_macro: 0.17914517754460846
precision_micro: 0.5374355326338254
precision_weighted: 0.28883695173740354
recall_macro: 0.3333333333333333
recall_micro: 0.5374355326338254
recall_weighted: 0.5374355326338254
accuracy: 0.5374355326338254
| {"tags": ["autotrain", "text-classification"], "datasets": ["autotrain-3cist-1i0ba/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]} | Akhil-9640/Telugu-AI4Bharath-Sentiment-Classification | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain",
"dataset:autotrain-3cist-1i0ba/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:48:14+00:00 |
text-generation | transformers | Quantizations of https://huggingface.co/NousResearch/Nous-Capybara-3B-V1.9
# From original readme
## 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|>
```
| {"language": ["en"], "license": "other", "tags": ["transformers", "gguf", "imatrix", "Nous-Capybara-3B-V1.9"], "pipeline_tag": "text-generation", "inference": false} | duyntnet/Nous-Capybara-3B-V1.9-imatrix-GGUF | null | [
"transformers",
"gguf",
"imatrix",
"Nous-Capybara-3B-V1.9",
"text-generation",
"en",
"license:other",
"region:us"
]
| null | 2024-04-28T05:49:50+00:00 |
text2text-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 ๐ค 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. -->
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## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | kssumanth6/t5_small_sentence_polishing_generator_v2 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:51:06+00:00 |
null | null | {} | Highwassed/finetuned_model | null | [
"region:us"
]
| null | 2024-04-28T05:51:09+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. -->
# llama2-poison-20p
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9493
## 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: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 16
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7273 | 1.0 | 520 | 0.9493 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.39.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"license": "llama2", "library_name": "peft", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrachat_200k"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "llama2-poison-20p", "results": []}]} | terry69/llama2-poison-20p | null | [
"peft",
"tensorboard",
"safetensors",
"llama",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
]
| null | 2024-04-28T05:51:27+00:00 |
null | null | {"license": "apache-2.0"} | AntonioAbrantes/llama-3-8b-bnb-4bit-aa.Q4_K_M.gguf | null | [
"gguf",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-28T05:51:28+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. -->
# G0428B1
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1184
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.238 | 0.09 | 10 | 1.8587 |
| 1.7567 | 0.18 | 20 | 1.5410 |
| 1.2688 | 0.27 | 30 | 0.8328 |
| 0.52 | 0.36 | 40 | 0.2500 |
| 0.1873 | 0.45 | 50 | 0.1579 |
| 0.1639 | 0.54 | 60 | 0.1524 |
| 0.1473 | 0.63 | 70 | 0.1475 |
| 0.1626 | 0.73 | 80 | 0.1470 |
| 0.1408 | 0.82 | 90 | 0.1486 |
| 0.1533 | 0.91 | 100 | 0.1471 |
| 0.1552 | 1.0 | 110 | 0.1467 |
| 0.1413 | 1.09 | 120 | 0.1467 |
| 0.1674 | 1.18 | 130 | 0.1451 |
| 0.1393 | 1.27 | 140 | 0.1416 |
| 0.1528 | 1.36 | 150 | 0.1378 |
| 0.1332 | 1.45 | 160 | 0.1366 |
| 0.1323 | 1.54 | 170 | 0.1349 |
| 0.1313 | 1.63 | 180 | 0.1329 |
| 0.1418 | 1.72 | 190 | 0.1308 |
| 0.1385 | 1.81 | 200 | 0.1281 |
| 0.1316 | 1.9 | 210 | 0.1258 |
| 0.1264 | 1.99 | 220 | 0.1262 |
| 0.1228 | 2.08 | 230 | 0.1231 |
| 0.1478 | 2.18 | 240 | 0.1223 |
| 0.1188 | 2.27 | 250 | 0.1213 |
| 0.1212 | 2.36 | 260 | 0.1210 |
| 0.1242 | 2.45 | 270 | 0.1212 |
| 0.1216 | 2.54 | 280 | 0.1201 |
| 0.1234 | 2.63 | 290 | 0.1192 |
| 0.1146 | 2.72 | 300 | 0.1186 |
| 0.1167 | 2.81 | 310 | 0.1184 |
| 0.1337 | 2.9 | 320 | 0.1184 |
| 0.1276 | 2.99 | 330 | 0.1184 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "gemma", "tags": ["generated_from_trainer"], "base_model": "google/gemma-2b", "model-index": [{"name": "G0428B1", "results": []}]} | Litzy619/G0428B1 | null | [
"safetensors",
"generated_from_trainer",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
]
| null | 2024-04-28T05:52:20+00:00 |
text-generation | transformers | {} | Vignav/llama-2-7b-cars-no-cot | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T05:52:29+00:00 |
|
null | null | {} | SharathKapilavai/llama-2-7b-tosca-trained | null | [
"region:us"
]
| null | 2024-04-28T05:53:08+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. -->
# mistral-7b-hf-platypus-lamini-vxxiii-chat-real_instruct_v2
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) 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: 0.0003
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.1
- Pytorch 2.2.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.1", "model-index": [{"name": "mistral-7b-hf-platypus-lamini-vxxiii-chat-real_instruct_v2", "results": []}]} | NassimB/mistral-7b-hf-platypus-lamini-vxxiii-chat-real_instruct_v2 | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-28T05:53:11+00:00 |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "t5-base"} | PQlet/T5base-lora-sumarizationTables-v2 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:t5-base",
"region:us"
]
| null | 2024-04-28T05:54:55+00:00 |
null | null | {"license": "openrail"} | KeroroK66/OokamiMio | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-28T05:55:07+00:00 |
|
text-generation | null |
# seawolf2357/Phi-3-mini-128k-instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`microsoft/Phi-3-mini-128k-instruct`](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) 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/microsoft/Phi-3-mini-128k-instruct) 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 seawolf2357/Phi-3-mini-128k-instruct-Q4_K_M-GGUF --model phi-3-mini-128k-instruct.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo seawolf2357/Phi-3-mini-128k-instruct-Q4_K_M-GGUF --model phi-3-mini-128k-instruct.Q4_K_M.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 phi-3-mini-128k-instruct.Q4_K_M.gguf -n 128
```
| {"language": ["en"], "license": "mit", "tags": ["nlp", "code", "llama-cpp", "gguf-my-repo"], "license_link": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/LICENSE", "pipeline_tag": "text-generation", "widget": [{"messages": [{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}]}]} | seawolf2357/Phi-3-mini-128k-instruct-Q4_K_M-GGUF | null | [
"gguf",
"nlp",
"code",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"license:mit",
"region:us"
]
| null | 2024-04-28T05:56:08+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/GodsonNtungi/Swahili_Gemma_vllm
<!-- 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/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.Q2_K.gguf) | Q2_K | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.IQ3_XS.gguf) | IQ3_XS | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.IQ3_S.gguf) | IQ3_S | 4.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.Q3_K_S.gguf) | Q3_K_S | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.IQ3_M.gguf) | IQ3_M | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.Q3_K_M.gguf) | Q3_K_M | 4.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.Q3_K_L.gguf) | Q3_K_L | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.IQ4_XS.gguf) | IQ4_XS | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.Q4_K_S.gguf) | Q4_K_S | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.Q4_K_M.gguf) | Q4_K_M | 5.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.Q5_K_S.gguf) | Q5_K_S | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.Q5_K_M.gguf) | Q5_K_M | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.Q6_K.gguf) | Q6_K | 7.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.Q8_0.gguf) | Q8_0 | 9.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Swahili_Gemma_vllm-GGUF/resolve/main/Swahili_Gemma_vllm.f16.gguf) | f16 | 17.2 | 16 bpw, overkill |
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"], "license": "apache-2.0", "library_name": "transformers", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "GodsonNtungi/Swahili_Gemma_vllm", "quantized_by": "mradermacher"} | mradermacher/Swahili_Gemma_vllm-GGUF | null | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:GodsonNtungi/Swahili_Gemma_vllm",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-28T05:57:18+00:00 |
null | null | {} | brankojaksic1/distilbert-base-uncased-finetuned-squad | null | [
"region:us"
]
| null | 2024-04-28T05:58:30+00:00 |
|
null | null | {} | NovaTsui/bluePencilXL_v500.safetensors | null | [
"region:us"
]
| null | 2024-04-28T05:59:58+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 ๐ถ 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: izaznov/ppo-Pyramids_Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
| {"library_name": "ml-agents", "tags": ["Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | izaznov/ppo-Pyramids_Training | null | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| null | 2024-04-28T06:00:17+00:00 |
text-to-image | diffusers | {} | GraydientPlatformAPI/js2prony-xl | null | [
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
]
| null | 2024-04-28T06:00:40+00:00 |
|
text-generation | transformers | {} | anyisalin/Meta-Llama-3-70B-Instruct-FP8-D | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T06:00:45+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 ๐ค 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": []} | Rimyy/Gemma-2b-finetuneGSMdata5ep | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T06:00:54+00:00 |
text-to-image | diffusers | {} | GraydientPlatformAPI/irislux4-xl | null | [
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
]
| null | 2024-04-28T06:01:22+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 ๐ค 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]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/o076lfp | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T06:01:40+00:00 |
null | null | {} | suakeler/cum_on_tongue | null | [
"region:us"
]
| null | 2024-04-28T06:02:41+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. -->
# 0.001_5iters_bs256_nodpo_only4w_iter_7
This model is a fine-tuned version of [ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_6](https://huggingface.co/ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_6) 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 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_6", "model-index": [{"name": "0.001_5iters_bs256_nodpo_only4w_iter_7", "results": []}]} | ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_7 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_6",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-28T06:02:51+00:00 |
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