Text Generation
Transformers
Safetensors
English
qwen3
text-generation-inference
unsloth
conversational
Instructions to use jerrycheng233/model4_dapo_16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jerrycheng233/model4_dapo_16bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jerrycheng233/model4_dapo_16bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("jerrycheng233/model4_dapo_16bit") model = AutoModelForMultimodalLM.from_pretrained("jerrycheng233/model4_dapo_16bit") 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 jerrycheng233/model4_dapo_16bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jerrycheng233/model4_dapo_16bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jerrycheng233/model4_dapo_16bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jerrycheng233/model4_dapo_16bit
- SGLang
How to use jerrycheng233/model4_dapo_16bit 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 "jerrycheng233/model4_dapo_16bit" \ --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": "jerrycheng233/model4_dapo_16bit", "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 "jerrycheng233/model4_dapo_16bit" \ --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": "jerrycheng233/model4_dapo_16bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use jerrycheng233/model4_dapo_16bit with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jerrycheng233/model4_dapo_16bit to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jerrycheng233/model4_dapo_16bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jerrycheng233/model4_dapo_16bit to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="jerrycheng233/model4_dapo_16bit", max_seq_length=2048, ) - Docker Model Runner
How to use jerrycheng233/model4_dapo_16bit with Docker Model Runner:
docker model run hf.co/jerrycheng233/model4_dapo_16bit
| {% macro render_content(content) %}{% if content is none %}{{- "" }}{% elif content is string %}{{- content }}{% elif content is mapping %}{{- content['value'] if 'value' in content else content['text'] }}{% elif content is iterable %}{% for item in content %}{% if item.type == 'text' %}{{- item['value'] if 'value' in item else item['text'] }}{% elif item.type == 'image' %}<im_patch>{% endif %}{% endfor %}{% endif %}{% endmacro %}{%- if tools %}{{- '<|im_start|>system | |
| ' }}{%- if messages[0]['role'] == 'system' %}{{- render_content(messages[0]['content']) }}{%- else %}{{- '' }}{%- endif %}{{- " | |
| # Tools | |
| You may call one or more functions to assist with the user query. | |
| You are provided with function signatures within <tools></tools> XML tags: | |
| <tools>" }}{%- for tool in tools %}{{- " | |
| " }}{{- tool | tojson }}{%- endfor %}{{- " | |
| </tools> | |
| For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags: | |
| <tool_call> | |
| {\"name\": <function-name>, \"arguments\": <args-json-object>} | |
| </tool_call><|im_end|> | |
| " }}{%- else %}{%- if messages[0]['role'] == 'system' %}{{- '<|im_start|>system | |
| ' + render_content(messages[0]['content']) + '<|im_end|> | |
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| ' + render_content(message.content) + '<|im_end|>' + ' | |
| ' }}{%- elif message.role == "assistant" %}{%- if loop.last %}{{- '<|im_start|>assistant | |
| <think> | |
| ' + render_content(message.reasoning_content) + ' | |
| </think> | |
| ' + render_content(message.content) }}{%- if message.tool_calls %}{%- for tool_call in message.tool_calls %}{%- set call_details = tool_call.function if tool_call.function is defined else tool_call %}{%- set tool_call_id = tool_call.id if tool_call.id is defined else tool_call.tool_call_id %}{{- ' | |
| <tool_call> | |
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| </tool_call>' }}{%- endfor %}{%- endif %}{{- '<|im_end|> | |
| ' }}{%- else %}{{- '<|im_start|>assistant | |
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| <tool_call> | |
| {"tool_call_id": "' + tool_call_id + '", "name": "' + call_details.name + '", "arguments": ' }}{% if call_details.arguments is string %}{{- call_details.arguments }}{% else %}{{- call_details.arguments | tojson }}{% endif %}{{- '} | |
| </tool_call>' }}{%- endfor %}{%- endif %}{{- '<|im_end|> | |
| ' }}{%- endif %} | |
| {%- elif message.role in ["tool_response", "tool"] %} | |
| {%- if loop.first or loop.previtem.role not in ["tool", "tool_response"] -%} | |
| {{- '<|im_start|>tool_response | |
| ' -}} | |
| {%- endif -%} | |
| {{- '<tool_response> | |
| ' + 'tool_call_id: ' + message.tool_call_id + ' | |
| ' + render_content(message.content) + ' | |
| </tool_response> | |
| ' -}} | |
| {%- if loop.last or loop.nextitem.role not in ["tool", "tool_response"] -%} | |
| {{- '<|im_end|> | |
| ' -}} | |
| {%- endif -%} | |
| {%- endif %}{%- endfor %}{%- if add_generation_prompt %}{{- '<|im_start|>assistant | |
| <think> | |
| ' }}{%- endif %} |