Instructions to use cloudyu/Mixtral_13B_Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudyu/Mixtral_13B_Chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cloudyu/Mixtral_13B_Chat")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cloudyu/Mixtral_13B_Chat") model = AutoModelForCausalLM.from_pretrained("cloudyu/Mixtral_13B_Chat") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cloudyu/Mixtral_13B_Chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cloudyu/Mixtral_13B_Chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cloudyu/Mixtral_13B_Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cloudyu/Mixtral_13B_Chat
- SGLang
How to use cloudyu/Mixtral_13B_Chat with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "cloudyu/Mixtral_13B_Chat" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cloudyu/Mixtral_13B_Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "cloudyu/Mixtral_13B_Chat" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cloudyu/Mixtral_13B_Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cloudyu/Mixtral_13B_Chat with Docker Model Runner:
docker model run hf.co/cloudyu/Mixtral_13B_Chat
Adding Evaluation Results
Browse filesThis is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr
The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.
If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions
README.md
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---
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license: mit
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---
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This is a fine-tuned 13B chat model
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Today, Yosemite National Park remains a testament to the power of individual actions combined with collective effort. Its pristine landscapes continue inspiring countless visitors each year, reminding us all that we have a responsibility towards safeguarding our planet for future generations. And amidst these stunning vistas stands Ava, proudly carrying forth John Muir's legacy, ensuring that his dream of preserving nature lives on forever.</s>
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-
```
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---
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license: mit
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model-index:
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- name: Mixtral_13B_Chat
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 67.41
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral_13B_Chat
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 85.87
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name: normalized accuracy
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+
source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral_13B_Chat
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 64.54
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral_13B_Chat
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 58.98
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral_13B_Chat
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 80.43
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral_13B_Chat
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 56.63
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name: accuracy
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+
source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=cloudyu/Mixtral_13B_Chat
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name: Open LLM Leaderboard
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---
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This is a fine-tuned 13B chat model
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Today, Yosemite National Park remains a testament to the power of individual actions combined with collective effort. Its pristine landscapes continue inspiring countless visitors each year, reminding us all that we have a responsibility towards safeguarding our planet for future generations. And amidst these stunning vistas stands Ava, proudly carrying forth John Muir's legacy, ensuring that his dream of preserving nature lives on forever.</s>
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```
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Mixtral_13B_Chat)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |68.98|
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|AI2 Reasoning Challenge (25-Shot)|67.41|
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|HellaSwag (10-Shot) |85.87|
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|MMLU (5-Shot) |64.54|
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+
|TruthfulQA (0-shot) |58.98|
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|Winogrande (5-shot) |80.43|
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|GSM8k (5-shot) |56.63|
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+
|