Instructions to use m-a-p/OpenCodeInterpreter-CL-34B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m-a-p/OpenCodeInterpreter-CL-34B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="m-a-p/OpenCodeInterpreter-CL-34B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("m-a-p/OpenCodeInterpreter-CL-34B") model = AutoModelForCausalLM.from_pretrained("m-a-p/OpenCodeInterpreter-CL-34B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use m-a-p/OpenCodeInterpreter-CL-34B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "m-a-p/OpenCodeInterpreter-CL-34B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "m-a-p/OpenCodeInterpreter-CL-34B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/m-a-p/OpenCodeInterpreter-CL-34B
- SGLang
How to use m-a-p/OpenCodeInterpreter-CL-34B 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 "m-a-p/OpenCodeInterpreter-CL-34B" \ --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": "m-a-p/OpenCodeInterpreter-CL-34B", "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 "m-a-p/OpenCodeInterpreter-CL-34B" \ --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": "m-a-p/OpenCodeInterpreter-CL-34B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use m-a-p/OpenCodeInterpreter-CL-34B with Docker Model Runner:
docker model run hf.co/m-a-p/OpenCodeInterpreter-CL-34B
File size: 1,921 Bytes
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"content": "β<MID>",
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"content": "β<EOT>",
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"β<EOT>"
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"eos_token": "</s>",
"eot_token": "β<EOT>",
"fill_token": "<FILL_ME>",
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"prefix_token": "β<PRE>",
"sp_model_kwargs": {},
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"suffix_first": false,
"suffix_token": "β<SUF>",
"tokenizer_class": "CodeLlamaTokenizer",
"unk_token": "<unk>",
"use_default_system_prompt": false
}
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