Instructions to use ylfeng/ReF-Decompile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ylfeng/ReF-Decompile with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ylfeng/ReF-Decompile") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ylfeng/ReF-Decompile") model = AutoModelForCausalLM.from_pretrained("ylfeng/ReF-Decompile") 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 ylfeng/ReF-Decompile with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ylfeng/ReF-Decompile" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ylfeng/ReF-Decompile", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ylfeng/ReF-Decompile
- SGLang
How to use ylfeng/ReF-Decompile 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 "ylfeng/ReF-Decompile" \ --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": "ylfeng/ReF-Decompile", "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 "ylfeng/ReF-Decompile" \ --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": "ylfeng/ReF-Decompile", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ylfeng/ReF-Decompile with Docker Model Runner:
docker model run hf.co/ylfeng/ReF-Decompile
| {%- if not tools is defined %} | |
| {%- set tools = none %} | |
| {%- endif %} | |
| {%- set user_messages = messages | selectattr("role", "equalto", "user") | list %} | |
| {%- for message in lmessages | rejectattr("role", "equalto", "tool") | rejectattr("role", "equalto", "tool_results") | selectattr("tool_calls", "undefined") %} | |
| {%- if (message["role"] == "user") != (loop.index0 % 2 == 0) %} | |
| {{- raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }} | |
| {%- endif %} | |
| {%- endfor %} | |
| {{- bos_token }} | |
| {%- for message in messages %} | |
| {%- if message["role"] == "user" %} | |
| {{- "[INST] " }} | |
| {%- if tools is not none and (message == user_messages[-1]) %} | |
| {{- "[AVAILABLE_TOOLS] [" }} | |
| {%- for tool in tools %} | |
| {%- set tool = tool.function %} | |
| {{- '{"type": "function", "function": {' }} | |
| {%- for key, val in tool.items() if key != "return" %} | |
| {%- if val is string %} | |
| {{- '"' + key + '": "' + val + '"' }} | |
| {%- else %} | |
| {{- '"' + key + '": ' + val|tojson }} | |
| {%- endif %} | |
| {%- if not loop.last %} | |
| {{- ", " }} | |
| {%- endif %} | |
| {%- endfor %} | |
| {{- "}}" }} | |
| {%- if not loop.last %} | |
| {{- ", " }} | |
| {%- else %} | |
| {{- "]" }} | |
| {%- endif %} | |
| {%- endfor %} | |
| {{- "[/AVAILABLE_TOOLS]" }} | |
| {%- endif %} | |
| {{- message["content"] + "[/INST]" }} | |
| {%- elif message["role"] == "tool_calls" or message.tool_calls is defined %} | |
| {%- if message.tool_calls is defined %} | |
| {%- set tool_calls = message.tool_calls %} | |
| {%- else %} | |
| {%- set tool_calls = message.content %} | |
| {%- endif %} | |
| {{- "[TOOL_CALLS] [" }} | |
| {%- for tool_call in tool_calls %} | |
| {%- set out = tool_call.function|tojson %} | |
| {{- out }} | |
| {%- if not loop.last %} | |
| {{- ", " }} | |
| {%- else %} | |
| {{- "]" }} | |
| {%- endif %} | |
| {%- endfor %} | |
| {%- elif message["role"] == "assistant" %} | |
| {{- " " + message["content"] }} | |
| {%- elif message["role"] == "tool_results" or message["role"] == "tool" %} | |
| {%- if message.content is defined and message.content.content is defined %} | |
| {%- set content = message.content.content %} | |
| {%- else %} | |
| {%- set content = message.content %} | |
| {%- endif %} | |
| {{- '[TOOL_RESULTS] {"content": ' + content|string + "}[/TOOL_RESULTS]" }} | |
| {%- else %} | |
| {{- raise_exception("Only user and assistant roles are supported, with the exception of an initial optional system message!") }} | |
| {%- endif %} | |
| {%- endfor %} | |