Text Generation
MLX
Safetensors
English
Chinese
qwen3_5
agnes
6bit
hybrid-attention
gated-delta-net
mtp
conversational
6-bit
Instructions to use hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP 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("hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP") 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 hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP"
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": "hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP 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 "hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP"
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 hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP"
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 "hermitdave/Agnes-3.0-Flash-MLX-6bit-MTP" \ --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"
Add MTP drafter usage instructions
Browse files
README.md
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Drop the folder under `~/.lmstudio/models/hermitdave/` in LM Studio and it appears as a `qwen3_5` model.
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## Attribution
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This conversion was produced by [Hermes Agent](https://hermes-agent.nousresearch.com) (Nous Research) — the autonomous research and conversion pipeline that identified the correct quantization parameters, fixed one-centered norm conversion, and validated output quality. Verified against the reference verison/Agnes-3.0-Flash-MLX-4bit model.
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Drop the folder under `~/.lmstudio/models/hermitdave/` in LM Studio and it appears as a `qwen3_5` model.
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## Speculative decoding with MTP drafter
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A companion MTP drafter is available for speculative decoding (up to 2× faster generation):
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```bash
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pip install mlx-vlm
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python -m mlx_vlm.server \
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--model hermitdave/Agnes-3.0-Flash-MLX-6bit \
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--draft-model hermitdave/Agnes-3.0-Flash-MTP-drafter
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```
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Or with the Python API:
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```python
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from mlx_lm import load
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from mlx_vlm.speculative.drafters.qwen3_5_mtp.config import Qwen3_5MTPConfig
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from mlx_vlm.speculative.drafters.qwen3_5_mtp.qwen3_5_mtp import Qwen3_5MTPDraftModel
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import json, mlx.core as mx, safetensors.torch
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base_model, tokenizer = load("hermitdave/Agnes-3.0-Flash-MLX-6bit")
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config = Qwen3_5MTPConfig.from_dict(json.load(open("path/to/Agnes-3.0-Flash-MTP-drafter/config.json")))
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mtp = Qwen3_5MTPDraftModel(config)
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weights = safetensors.torch.load_file("path/to/Agnes-3.0-Flash-MTP-drafter/model.safetensors")
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mtp.load_weights([(k, mx.array(v)) for k, v in weights.items()])
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mtp.bind(base_model)
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```
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The drafter is architecture-compatible with Qwen3.5's gated attention and was extracted from the original Agnes-3.0-Flash model. See the [MTP drafter repo](https://huggingface.co/hermitdave/Agnes-3.0-Flash-MTP-drafter) for details.
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## Attribution
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This conversion was produced by [Hermes Agent](https://hermes-agent.nousresearch.com) (Nous Research) — the autonomous research and conversion pipeline that identified the correct quantization parameters, fixed one-centered norm conversion, and validated output quality. Verified against the reference verison/Agnes-3.0-Flash-MLX-4bit model.
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