Instructions to use PTPReasoning/Qwen2.5-7B-Base-RL-Baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PTPReasoning/Qwen2.5-7B-Base-RL-Baseline with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PTPReasoning/Qwen2.5-7B-Base-RL-Baseline") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("PTPReasoning/Qwen2.5-7B-Base-RL-Baseline") model = AutoModelForCausalLM.from_pretrained("PTPReasoning/Qwen2.5-7B-Base-RL-Baseline", device_map="auto") 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 Settings
- vLLM
How to use PTPReasoning/Qwen2.5-7B-Base-RL-Baseline with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PTPReasoning/Qwen2.5-7B-Base-RL-Baseline" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PTPReasoning/Qwen2.5-7B-Base-RL-Baseline", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/PTPReasoning/Qwen2.5-7B-Base-RL-Baseline
- SGLang
How to use PTPReasoning/Qwen2.5-7B-Base-RL-Baseline 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 "PTPReasoning/Qwen2.5-7B-Base-RL-Baseline" \ --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": "PTPReasoning/Qwen2.5-7B-Base-RL-Baseline", "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 "PTPReasoning/Qwen2.5-7B-Base-RL-Baseline" \ --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": "PTPReasoning/Qwen2.5-7B-Base-RL-Baseline", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use PTPReasoning/Qwen2.5-7B-Base-RL-Baseline with Docker Model Runner:
docker model run hf.co/PTPReasoning/Qwen2.5-7B-Base-RL-Baseline
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| "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'A conversation between User and Assistant. The User asks a question, and the Assistant solves it. The assistant first reasons through the problem by generating high-level partial programs with key parts hidden using \"...\" markers. It then simulates programs trace based on the incomplete partial programs. The partial program must be general enough to solve all instances of the problem type, not just specific examples. The partial programs and traces are enclosed within <partial_program> </partial_program> and <program_trace> </program_trace> tags, while the overall reasoning process and final answer are enclosed within <think> </think> and <answer> </answer> tags, respectively. You should also wrap your final answer in $\\\\boxed{{ANSWER}}$ if it is a mathematical expression.\\n\\nFormat:\\n<think>\\n<partial_program>\\n[Partial Program here]\\n</partial_program>\\n<program_trace>\\n[Program Trace here]\\n</program_trace>\\n</think>\\n<answer>\\n[Final Answer here]\\n</answer>' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nA conversation between User and Assistant. The User asks a question, and the Assistant solves it. The assistant first reasons through the problem by generating high-level partial programs with key parts hidden using \"...\" markers. It then simulates programs trace based on the incomplete partial programs. The partial program must be general enough to solve all instances of the problem type, not just specific examples. The partial programs and traces are enclosed within <partial_program> </partial_program> and <program_trace> </program_trace> tags, while the overall reasoning process and final answer are enclosed within <think> </think> and <answer> </answer> tags, respectively. You should also wrap your final answer in $\\\\boxed{{ANSWER}}$ if it is a mathematical expression.\\n\\nFormat:\\n<think>\\n<partial_program>\\n[Partial Program here]\\n</partial_program>\\n<program_trace>\\n[Program Trace here]\\n</program_trace>\\n</think>\\n<answer>\\n[Final Answer here]\\n</answer><|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|endoftext|>", | |
| "errors": "replace", | |
| "extra_special_tokens": {}, | |
| "model_max_length": 131072, | |
| "pad_token": "<|endoftext|>", | |
| "split_special_tokens": false, | |
| "tokenizer_class": "Qwen2Tokenizer", | |
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| } | |