Instructions to use SenseLLM/ReflectionCoder-CL-34B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SenseLLM/ReflectionCoder-CL-34B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SenseLLM/ReflectionCoder-CL-34B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SenseLLM/ReflectionCoder-CL-34B") model = AutoModelForCausalLM.from_pretrained("SenseLLM/ReflectionCoder-CL-34B") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SenseLLM/ReflectionCoder-CL-34B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SenseLLM/ReflectionCoder-CL-34B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SenseLLM/ReflectionCoder-CL-34B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SenseLLM/ReflectionCoder-CL-34B
- SGLang
How to use SenseLLM/ReflectionCoder-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 "SenseLLM/ReflectionCoder-CL-34B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SenseLLM/ReflectionCoder-CL-34B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "SenseLLM/ReflectionCoder-CL-34B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SenseLLM/ReflectionCoder-CL-34B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SenseLLM/ReflectionCoder-CL-34B with Docker Model Runner:
docker model run hf.co/SenseLLM/ReflectionCoder-CL-34B
File size: 3,950 Bytes
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"content": "β<EOT>",
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"β<EOT>",
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],
"bos_token": "<s>",
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"eot_token": "β<EOT>",
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"tokenizer_class": "CodeLlamaTokenizer",
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"chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{ '<|user|>' }}{% elif message['role'] == 'system' %}{{ '<|system|>' }}{% elif message['role'] == 'assistant' %}{{ '<|assistant|>' }}{% endif %}{% if message['content'] is string %}{{ '<|text|>' + message['content'] + '<|endofblock|>' }}{% elif message['content'] is sequence %}{% for block in message['content'] %}{% if block['type'] == 'text' %}{{ '<|text|>' }}{% elif block['type'] == 'code' %}{{ '<|code|>' }}{% elif block['type'] == 'execution' %}{{ '<|execution|>' }}{% endif %}{{ block['content'] + '<|endofblock|>' }}{% endfor %}{% endif %}{{ '<|endofmessage|>' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>' }}{% endif %}"
}
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