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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
| language: | |
| - en | |
| license: apache-2.0 | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| base_model: | |
| - uukuguy/speechless-code-mistral-7b-v1.0 | |
| - upaya07/Arithmo2-Mistral-7B | |
| pipeline_tag: text-generation | |
| model-index: | |
| - name: sethuiyer/CodeCalc-Mistral-7B | |
| results: | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: AI2 Reasoning Challenge (25-Shot) | |
| type: ai2_arc | |
| config: ARC-Challenge | |
| split: test | |
| args: | |
| num_few_shot: 25 | |
| metrics: | |
| - type: acc_norm | |
| value: 61.95 | |
| name: normalized accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: HellaSwag (10-Shot) | |
| type: hellaswag | |
| split: validation | |
| args: | |
| num_few_shot: 10 | |
| metrics: | |
| - type: acc_norm | |
| value: 83.64 | |
| name: normalized accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: MMLU (5-Shot) | |
| type: cais/mmlu | |
| config: all | |
| split: test | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 62.78 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: TruthfulQA (0-shot) | |
| type: truthful_qa | |
| config: multiple_choice | |
| split: validation | |
| args: | |
| num_few_shot: 0 | |
| metrics: | |
| - type: mc2 | |
| value: 47.49 | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: Winogrande (5-shot) | |
| type: winogrande | |
| config: winogrande_xl | |
| split: validation | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 78.3 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: GSM8k (5-shot) | |
| type: gsm8k | |
| config: main | |
| split: test | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 63.53 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/CodeCalc-Mistral-7B | |
| name: Open LLM Leaderboard | |
| # CodeCalc-Mistral-7B | |
| <p align="center"> | |
| <img src="https://huggingface.co/sethuiyer/CodeCalc-Mistral-7B/resolve/main/codecalc.webp" height="128px" alt="CodeCalc"> | |
| </p> | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| base_model: uukuguy/speechless-code-mistral-7b-v1.0 | |
| dtype: bfloat16 | |
| merge_method: ties | |
| models: | |
| - model: uukuguy/speechless-code-mistral-7b-v1.0 | |
| - model: upaya07/Arithmo2-Mistral-7B | |
| parameters: | |
| density: [0.25, 0.35, 0.45, 0.35, 0.25] | |
| weight: [0.1, 0.25, 0.5, 0.25, 0.1] | |
| parameters: | |
| int8_mask: true | |
| ``` | |
| ### Evaluation | |
| | T | Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | | |
| |----|---------------------------------------------|---------|------|-----------|-------|------------|------------|-------| | |
| | 🔍 | sethuiyer/CodeCalc-Mistral-7B | 66.33 | 61.95| 83.64 | 62.78 | 47.79 | 78.3 | 63.53 | | |
| | 📉 | uukuguy/speechless-code-mistral-7b-v1.0 | 63.6 | 61.18| 83.77 | 63.4 | 47.9 | 78.37 | 47.01 | | |
| The merge appears to be successful, especially considering the substantial improvement in the GSM8K benchmark while maintaining comparable performance on other metrics. | |
| ## Usage | |
| Alpaca Instruction Format and [Divine Intellect](https://raw.githubusercontent.com/oobabooga/text-generation-webui/ae8cd449ae3e0236ecb3775892bb1eea23f9ed68/presets/Divine%20Intellect.yaml) preset. | |
| ``` | |
| You are an intelligent programming assistant. | |
| ### Instruction: | |
| Implement a linked list in C++ | |
| ### Response: | |
| ``` | |
| Preset: | |
| ```text | |
| temperature: 1.31 | |
| top_p: 0.14 | |
| repetition_penalty: 1.17 | |
| top_k: 49 | |
| ``` | |
| # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) | |
| Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_sethuiyer__CodeCalc-Mistral-7B) | |
| | Metric |Value| | |
| |---------------------------------|----:| | |
| |Avg. |66.33| | |
| |AI2 Reasoning Challenge (25-Shot)|61.95| | |
| |HellaSwag (10-Shot) |83.64| | |
| |MMLU (5-Shot) |62.78| | |
| |TruthfulQA (0-shot) |47.79| | |
| |Winogrande (5-shot) |78.30| | |
| |GSM8k (5-shot) |63.53| | |