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 sglang/Dockerfile from RecursiveSI/flint: direct link, hf CLI and curl.
- Browser
- Download file 855 Bytes
-
https://huggingface.co/RecursiveSI/flint/resolve/main/sglang/Dockerfile
- Command line
-
hf download hf://RecursiveSI/flint/sglang/Dockerfile
-
curl -L -o Dockerfile https://huggingface.co/RecursiveSI/flint/resolve/main/sglang/Dockerfile
855 Bytes
| # Flint serving image: SGLang v0.5.16 + the flint_sglang model package + the FA4 learned-sink fix. | |
| # Build from the directory that contains this file, install.sh, the flint_sglang wheel and the patch: | |
| # docker build -t flint-sglang . | |
| # Host requirement: NVIDIA driver >= 580 (CUDA 13), or CUDA forward compatibility on older | |
| # datacenter drivers (the image ships it in /usr/local/cuda/compat). | |
| # lmsysorg/sglang:v0.5.16-cu130 (multi-arch index digest the kit was validated with) | |
| FROM lmsysorg/sglang@sha256:7b6a35df9839fd593a94a1eaee82d7777f472225d9f3ad1f8a2e0cb2bd1785d0 | |
| COPY install.sh fa4-learned-sink-fix.patch flint_sglang-*.whl /tmp/flint-build/ | |
| RUN bash /tmp/flint-build/install.sh /tmp/flint-build && rm -rf /tmp/flint-build | |
| ENV FLASHINFER_VERSION=0.6.16.post1 \ | |
| SGLANG_EXTERNAL_MODEL_PACKAGE=flint_sglang \ | |
| FLASHINFER_USE_CUDA_NORM=1 | |