Image-Text-to-Text
MLX
Safetensors
English
step3p7
stepfun
step-3.7-flash
vision-language
multimodal
Mixture of Experts
quantized
mxfp4
microscaling
fp4
apple-silicon
text-generation
conversational
custom_code
4-bit precision
Instructions to use osmapi/Step-3.7-Flash-MXFP4-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use osmapi/Step-3.7-Flash-MXFP4-mlx with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("osmapi/Step-3.7-Flash-MXFP4-mlx") config = load_config("osmapi/Step-3.7-Flash-MXFP4-mlx") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Pi
How to use osmapi/Step-3.7-Flash-MXFP4-mlx with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "osmapi/Step-3.7-Flash-MXFP4-mlx"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "osmapi/Step-3.7-Flash-MXFP4-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use osmapi/Step-3.7-Flash-MXFP4-mlx 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 "osmapi/Step-3.7-Flash-MXFP4-mlx"
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 osmapi/Step-3.7-Flash-MXFP4-mlx
Run Hermes
hermes
Add public Step-3.7 variant links
Browse files
README.md
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No fine-tuning, distillation, or retraining was applied. The upstream StepFun checkpoint was downloaded and verified locally, then eligible text and vision `.weight` tensors were converted with MLX `mode="mxfp4"` quantization. Tokenizer, chat template, custom Step3.7 Python modules, and non-quantized control tensors are preserved from the source release.
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## Compatibility Status
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This upload is a standard MLX MXFP4 safetensors bundle, but it is not yet a drop-in `mlx_lm.load(...)` or `mlx_vlm.load(...)` model.
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No fine-tuning, distillation, or retraining was applied. The upstream StepFun checkpoint was downloaded and verified locally, then eligible text and vision `.weight` tensors were converted with MLX `mode="mxfp4"` quantization. Tokenizer, chat template, custom Step3.7 Python modules, and non-quantized control tensors are preserved from the source release.
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## Public osmAPI Step-3.7-Flash Variants
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| Variant | Repository | Format | Notes |
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| MXFP4 MLX | [osmapi/Step-3.7-Flash-MXFP4-mlx](https://huggingface.co/osmapi/Step-3.7-Flash-MXFP4-mlx) | MLX MXFP4 safetensors | Public 4-bit microscaling tensor bundle |
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| OptiQ 3.7bpw MLX | [osmapi/Step-3.7-Flash-OptiQ-3.7bpw-mlx](https://huggingface.co/osmapi/Step-3.7-Flash-OptiQ-3.7bpw-mlx) | MLX affine mixed-precision safetensors | Public 3.7 BPW OptiQ tensor bundle |
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## Compatibility Status
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This upload is a standard MLX MXFP4 safetensors bundle, but it is not yet a drop-in `mlx_lm.load(...)` or `mlx_vlm.load(...)` model.
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