Instructions to use bouroo/flint-mlx-mxfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use bouroo/flint-mlx-mxfp4 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("bouroo/flint-mlx-mxfp4") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use bouroo/flint-mlx-mxfp4 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "bouroo/flint-mlx-mxfp4"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "bouroo/flint-mlx-mxfp4" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use bouroo/flint-mlx-mxfp4 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "bouroo/flint-mlx-mxfp4"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "bouroo/flint-mlx-mxfp4" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bouroo/flint-mlx-mxfp4", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use bouroo/flint-mlx-mxfp4 with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "bouroo/flint-mlx-mxfp4"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default bouroo/flint-mlx-mxfp4
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use bouroo/flint-mlx-mxfp4 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "bouroo/flint-mlx-mxfp4"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "bouroo/flint-mlx-mxfp4" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Flint 40B-A2.5B, MXFP4 for MLX
MXFP4 quantization of RecursiveSI/flint for Apple Silicon via MLX.
| Base | RecursiveSI/flint (BF16, 79.6 GB) |
| Format | MLX mxfp4, group size 32, 4 bit |
| Size | see the file list (about 20 GB) |
| Kept BF16 | mlp.gate routers, embed_tokens, lm_head, all norms, softmax_offset |
| Experts | 256 routed, unpruned, stacked into switch_mlp.* |
| Architecture | 39.8B total / ~2.5B active, 49 layers (2 dense + 47 MoE), sliding window 513 |
Sampling
Mirror the upstream card: temperature 1.0, top_p 0.95, top_k 20.
generation_config.json already carries these.
Reasoning tokens
Reasoning counts toward max_tokens. A coding prompt that thinks for 900 tokens needs
max_tokens above that or the reply returns with finish_reason: "length" and empty
content.
Template note
chat_template.jinja here is the upstream template with one change: the
replace("\x00", "") filter is written with a literal NUL byte instead of the \x00
escape. Engines that reject backslash escapes in templates (LM Studio / MLX, error
Unexpected escaped character: x) cannot render the upstream file at all. Output is
identical on engines that accept both forms.
Known limitation
The tokenizer has no Thai (or other non-Latin script) vocabulary: 0 of 150144 vocab
entries contain a codepoint in U+0E00..U+0E7F. Thai input byte-fragments into mojibake
and the model degenerates. This is inherited from the base checkpoint, not introduced by
the quantization.
Conversion
Expert weights were stacked per layer from the sharded BF16 checkpoint and quantized one layer at a time; nothing was fine-tuned, and no calibration data was used.
- Downloads last month
- -
Quantized
Model tree for bouroo/flint-mlx-mxfp4
Base model
RecursiveSI/flint