RecursiveSI/flint
text-generationtransformersVerified checksumstransformerssafetensorsflinttext-generationconversationallicense:apache-2.0
- License
- apache-2.0
- Size
- 74 GB
- Revision
- main
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All models by RecursiveSI →Mirrored from RecursiveSI/flint
All models by RecursiveSI →[Image blocked: Flint logo]
Blog | Hugging Face | Local engine | Documentation
Flint is Recursive’s mixture-of-experts language model for coding agents, with 39.8 billion total parameters and approximately 2.5 billion active per token. It is designed to investigate codebases, fix bugs, implement features, and write tests.
Flint supports text input and output, reasoning, and multi-turn tool use. In our release evaluations, it scores 54.9% on Terminal-Bench 2.1, 89.7% on SWE-bench Pro v2, and 74.2% on SWE-bench Verified.
This repository contains the BF16 checkpoint, model configuration, tokenizer, chat template, and an SGLang serving kit. Quantized variants are prepared separately for NVIDIA and Apple Silicon runtimes.
[Image blocked: Flint benchmark performance: BF16 and NVFP4 checkpoints compared with local models that run on similar hardware]
Flint results use temperature 1.0, top-p 0.95, and top-k 20. Terminal-Bench uses Terminus 2; SWE-bench uses mini-swe-agent. Agentic scores report average pass@1 over at least five attempts. † marks scores measured by Recursive; other comparison scores are developer-reported.
Evaluation setups vary across sources. Flint’s Terminal-Bench score counts solutions obtained through web retrieval as unsuccessful; developer-reported scores may not apply the same adjustment.
¹ The active-parameter convention includes the output head and excludes the full input embedding matrix. The active body alone is approximately 2.2B parameters. “40B-A2.5B” is the rounded model-size designation.
Flint runs on one B300 or H200 GPU per replica with the SGLang kit in sglang/. Transformers modeling code is not included yet.
sglang/README.md [blocked] covers requirements, serve-script options, multi-GPU deployments, and API details.
The server applies these sampling settings when a request omits them. Flint supports OpenAI-compatible tool calls. Use the checkpoint’s chat template and preserve returned reasoning_content in conversation history. Prompt and output share the context window.
Flint was trained from scratch through pretraining, mid-training, supervised fine-tuning, and reinforcement learning. Training includes text and code, instruction following, and agentic tasks.
Flint’s supervised fine-tuning included safety-oriented examples, but the model can still produce incorrect or harmful content, faulty code, or inappropriate tool calls. Test generated code and review consequential changes before applying them. Tool-using applications should enforce their own permissions and execution boundaries; structured tool-call grammar does not prevent the model from choosing an unsafe or incorrect action.
Long reasoning can increase latency and exhaust the output budget before an answer is complete. The context configuration is a capacity limit, not a guarantee of uniform accuracy across all sequence lengths or tasks. Results can vary with checkpoint precision and inference runtime; evaluate the specific checkpoint and runtime you intend to deploy.
Flint is released under the Apache 2.0 license [blocked]. Hosted API terms and data practices are separate from the weight license.
For model and serving issues, contact support@recursive.com and include the checkpoint revision, runtime version, hardware, launch command, and a minimal reproduction. Report safety concerns or suspected misuse to safety@recursive.com, and security vulnerabilities to security@recursive.com.
pip install -U huggingface_hub openaihf download RecursiveSI/flint --local-dir flint && cd flint # about 80 GBdocker build -t flint-sglang sglangdocker run -d --name flint --gpus '"device=0"' --ipc=host -p 127.0.0.1:30000:30000 \ -v "$PWD:/model" -e MODEL=/model flint-sglang bash /model/sglang/serve_b300.sh # H200: serve_h200.shuntil curl -sf localhost:30000/health_generate >/dev/null; do sleep 15; done # progress: docker logs -f flintpython3 sglang/example_client.py --base-url http://localhost:30000/v1 # reasoning and a tool callfrom openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="unused")response = client.chat.completions.create( model="flint", messages=[{"role": "user", "content": "Write a Python function that merges overlapping intervals."}], max_tokens=32768,)print(response.choices[0].message.content)