Instructions to use RecursiveSI/flint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RecursiveSI/flint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RecursiveSI/flint") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RecursiveSI/flint") model = AutoModelForCausalLM.from_pretrained("RecursiveSI/flint", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RecursiveSI/flint with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RecursiveSI/flint" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RecursiveSI/flint", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/RecursiveSI/flint
- SGLang
How to use RecursiveSI/flint 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 "RecursiveSI/flint" \ --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": "RecursiveSI/flint", "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 "RecursiveSI/flint" \ --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": "RecursiveSI/flint", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use RecursiveSI/flint with Docker Model Runner:
docker model run hf.co/RecursiveSI/flint
Download chat_template.jinja from RecursiveSI/flint: direct link, hf CLI and curl.
- Browser
- Download file 2.33 kB
-
https://huggingface.co/RecursiveSI/flint/resolve/main/chat_template.jinja
- Command line
-
hf download hf://RecursiveSI/flint/chat_template.jinja
-
curl -L -o chat_template.jinja https://huggingface.co/RecursiveSI/flint/resolve/main/chat_template.jinja
2.33 kB
| {%- macro flint_text(t) -%} | |
| {%- if t is string -%} | |
| {{- t | replace("\x00", "") | replace("<think>", "") | replace("</think>", "") | replace("<|begin_of_text|>", "<|begin_of_text|>") | replace("<|end_of_text|>", "<|end_of_text|>") | replace("<|system|>", "<|system|>") | replace("<|developer|>", "<|developer|>") | replace("<|user|>", "<|user|>") | replace("<|assistant|>", "<|assistant|>") | replace("<|observation|>", "<|observation|>") -}} | |
| {%- else -%} | |
| {{- t -}} | |
| {%- endif -%} | |
| {%- endmacro -%} | |
| {%- set tool_preamble -%} | |
| {%- if tools %}<|system|> | |
| # 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 %} | |
| {%- set fn = tool.function if tool.function is defined else tool %} | |
| {{ fn | tojson(ensure_ascii=False) }} | |
| {%- endfor %} | |
| </tools> | |
| For each function call, output the function name and arguments within the following XML format: | |
| <tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value>...</tool_call> | |
| {% endif -%} | |
| {%- endset -%} | |
| {%- set ns = namespace(tools_pending=true) -%} | |
| {%- for m in messages -%} | |
| {%- if ns.tools_pending and m.role != 'system' -%} | |
| {{ tool_preamble }} | |
| {%- set ns.tools_pending = false -%} | |
| {%- endif -%} | |
| {%- if m.role == 'system' -%} | |
| <|system|>{{ flint_text(m.content) }} | |
| {%- elif m.role == 'developer' -%} | |
| <|developer|>{{ flint_text(m.content) }} | |
| {%- elif m.role == 'user' -%} | |
| <|user|>{{ flint_text(m.content) }} | |
| {%- elif m.role == 'assistant' -%} | |
| <|assistant|> | |
| {%- if m.reasoning_content %}<think>{{ m.reasoning_content }}</think>{% endif -%} | |
| {%- if m.content %}{{ m.content }}{% endif -%} | |
| {%- if m.tool_calls -%} | |
| {%- for tc in m.tool_calls -%} | |
| {%- set fn = tc.function if tc.function is defined else tc -%} | |
| <tool_call>{{ fn.name }} | |
| {%- for k, v in fn.arguments.items() -%} | |
| <arg_key>{{ k }}</arg_key><arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value> | |
| {%- endfor -%} | |
| </tool_call> | |
| {%- endfor -%} | |
| {%- endif -%} | |
| {%- elif m.role == 'tool' -%} | |
| <|observation|><tool_response>{{ flint_text(m.content) }}</tool_response> | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- if ns.tools_pending -%} | |
| {{ tool_preamble }} | |
| {%- endif -%} | |
| {%- if add_generation_prompt -%} | |
| <|assistant|> | |
| {%- endif -%} | |