Text Generation
Transformers
Safetensors
English
mistral
mergekit
Merge
Eval Results (legacy)
text-generation-inference
Instructions to use sethuiyer/CodeCalc-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sethuiyer/CodeCalc-Mistral-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sethuiyer/CodeCalc-Mistral-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sethuiyer/CodeCalc-Mistral-7B") model = AutoModelForCausalLM.from_pretrained("sethuiyer/CodeCalc-Mistral-7B") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use sethuiyer/CodeCalc-Mistral-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sethuiyer/CodeCalc-Mistral-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sethuiyer/CodeCalc-Mistral-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sethuiyer/CodeCalc-Mistral-7B
- SGLang
How to use sethuiyer/CodeCalc-Mistral-7B 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 "sethuiyer/CodeCalc-Mistral-7B" \ --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": "sethuiyer/CodeCalc-Mistral-7B", "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 "sethuiyer/CodeCalc-Mistral-7B" \ --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": "sethuiyer/CodeCalc-Mistral-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sethuiyer/CodeCalc-Mistral-7B with Docker Model Runner:
docker model run hf.co/sethuiyer/CodeCalc-Mistral-7B
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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library_name: transformers
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tags:
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- merge
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model-index:
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results:
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value: 61.95
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name: normalized accuracy
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source:
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url:
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 83.64
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name: normalized accuracy
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source:
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B
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name: Open LLM Leaderboard
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type: text-generation
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value: 62.78
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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- type: mc2
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value: 47.49
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source:
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url:
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 78.3
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name: accuracy
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 63.53
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name: accuracy
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B
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name: Open LLM Leaderboard
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pipeline_tag: text-generation
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---
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# CodeCalc-Mistral-7B
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top_k: 49
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```
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language:
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license: apache-2.0
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library_name: transformers
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tags:
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- mergekit
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- merge
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base_model:
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- uukuguy/speechless-code-mistral-7b-v1.0
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- upaya07/Arithmo2-Mistral-7B
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pipeline_tag: text-generation
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model-index:
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- name: sethuiyer/CodeCalc-Mistral-7B
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results:
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value: 61.95
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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=sethuiyer/CodeCalc-Mistral-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 83.64
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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=sethuiyer/CodeCalc-Mistral-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 62.78
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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=sethuiyer/CodeCalc-Mistral-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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- type: mc2
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value: 47.49
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 78.3
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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=sethuiyer/CodeCalc-Mistral-7B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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value: 63.53
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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=sethuiyer/CodeCalc-Mistral-7B
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name: Open LLM Leaderboard
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---
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# CodeCalc-Mistral-7B
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top_k: 49
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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_sethuiyer__CodeCalc-Mistral-7B)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |66.33|
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|AI2 Reasoning Challenge (25-Shot)|61.95|
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|HellaSwag (10-Shot) |83.64|
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|MMLU (5-Shot) |62.78|
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|TruthfulQA (0-shot) |47.79|
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|Winogrande (5-shot) |78.30|
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|GSM8k (5-shot) |63.53|
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