Instructions to use HelpingAI/hai3.1-checkpoint-0002 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HelpingAI/hai3.1-checkpoint-0002 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HelpingAI/hai3.1-checkpoint-0002", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("HelpingAI/hai3.1-checkpoint-0002", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HelpingAI/hai3.1-checkpoint-0002 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HelpingAI/hai3.1-checkpoint-0002" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HelpingAI/hai3.1-checkpoint-0002", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/HelpingAI/hai3.1-checkpoint-0002
- SGLang
How to use HelpingAI/hai3.1-checkpoint-0002 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 "HelpingAI/hai3.1-checkpoint-0002" \ --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": "HelpingAI/hai3.1-checkpoint-0002", "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 "HelpingAI/hai3.1-checkpoint-0002" \ --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": "HelpingAI/hai3.1-checkpoint-0002", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use HelpingAI/hai3.1-checkpoint-0002 with Docker Model Runner:
docker model run hf.co/HelpingAI/hai3.1-checkpoint-0002
Download tokenizer_config.json from HelpingAI/hai3.1-checkpoint-0002: direct link, hf CLI and curl.
- Browser
- Download file 8.22 kB
-
https://huggingface.co/HelpingAI/hai3.1-checkpoint-0002/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://HelpingAI/hai3.1-checkpoint-0002/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/HelpingAI/hai3.1-checkpoint-0002/resolve/main/tokenizer_config.json
8.22 kB
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| "151665": { | |
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| }, | |
| "additional_special_tokens": [ | |
| "<|im_start|>", | |
| "<|im_end|>", | |
| "<|object_ref_start|>", | |
| "<|object_ref_end|>", | |
| "<|box_start|>", | |
| "<|box_end|>", | |
| "<|quad_start|>", | |
| "<|quad_end|>", | |
| "<|vision_start|>", | |
| "<|vision_end|>", | |
| "<|vision_pad|>", | |
| "<|image_pad|>", | |
| "<|video_pad|>" | |
| ], | |
| "bos_token": null, | |
| "chat_template": "{%- if tools %}\n<|im_start|>system\n{%- if messages[0].role == 'system' %}\n{{ messages[0].content }}\n\n{%- endif %}\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>{%- for tool in tools %}\n{{ tool | tojson }}{%- endfor %}\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{%- else %}{%- if messages[0].role == 'system' %}\n<|im_start|>system\n{{ messages[0].content }}<|im_end|>\n{%- endif %}{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for forward_message in messages %}{%- set index = (messages|length - 1) - loop.index0 %}{%- set message = messages[index] %}{%- set current_content = message.content if message.content is not none else '' %}{%- set tool_start = '<tool_response>' %}{%- set tool_start_length = tool_start|length %}{%- set start_of_message = current_content[:tool_start_length] %}{%- set tool_end = '</tool_response>' %}{%- set tool_end_length = tool_end|length %}{%- set start_pos = (current_content|length) - tool_end_length %}{%- if start_pos < 0 %}{%- set start_pos = 0 %}{%- endif %}{%- set end_of_message = current_content[start_pos:] %}{%- if ns.multi_step_tool and message.role == \"user\" and not(start_of_message == tool_start and end_of_message == tool_end) %}{%- set ns.multi_step_tool = false %}{%- set ns.last_query_index = index %}{%- endif %}{%- endfor %}\n{%- for message in messages %}{%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}<|im_start|>{{ message.role }}\n{{ message.content }}<|im_end|>\n{%- elif message.role == \"assistant\" %}<|im_start|>assistant\n{%- if message.content %}{{ message.content }}{%- endif %}{%- if message.tool_calls %}{%- for tool_call in message.tool_calls %}{%- if (loop.first and content) or (not loop.first) %}\n{%- endif %}{%- if tool_call.function %}{%- set tool_call = tool_call.function %}{%- endif %}<tool_call>\n{\"name\": \"{{ tool_call.name }}\", \"arguments\": {{ tool_call.arguments if tool_call.arguments is string else tool_call.arguments | tojson }}}\n</tool_call>{%- endfor %}{%- endif %}<|im_end|>\n{%- elif message.role == \"tool\" %}{%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}<|im_start|>user{%- endif %}\n<tool_response>\n{{ message.content }}\n</tool_response>{%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}<|im_end|>\n{%- endif %}{%- endif %}{%- endfor %}\n{%- if add_generation_prompt %}<|im_start|>assistant\n{%- endif %}", | |
| "eos_token": "<|im_end|>", | |
| "errors": "replace", | |
| "clean_up_tokenization_spaces": false, | |
| "extra_special_tokens": {}, | |
| "model_max_length": 40960, | |
| "pad_token": "<|vision_pad|>", | |
| "padding_side": "right", | |
| "split_special_tokens": false, | |
| "tokenizer_class": "Qwen2Tokenizer", | |
| "unk_token": null | |
| } |