Instructions to use hermitdave/Agnes-3.0-Flash-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hermitdave/Agnes-3.0-Flash-MLX-4bit 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-4bit")

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

How to use hermitdave/Agnes-3.0-Flash-MLX-4bit 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-4bit"

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-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use hermitdave/Agnes-3.0-Flash-MLX-4bit 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-4bit"

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-4bit"
# 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-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use hermitdave/Agnes-3.0-Flash-MLX-4bit 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-4bit"

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-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use hermitdave/Agnes-3.0-Flash-MLX-4bit 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-4bit"

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-4bit" \
  --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"

Agnes-3.0-Flash-MLX-4bit / README.md

hermitdave

Add oMLX MTP limitation note

14e85ef verified 16 days ago

preview code

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	---
	license: apache-2.0
	base_model: Agnes-AI/Agnes-3.0-Flash
	library_name: mlx
	pipeline_tag: text-generation
	tags:
	- mlx
	- qwen3_5
	- agnes
	- 4bit
	- hybrid-attention
	- gated-delta-net
	language:
	- en
	- zh
	---

	# Agnes-3.0-Flash — MLX 4-bit

	4-bit MLX quantization of [Agnes-AI/Agnes-3.0-Flash](https://huggingface.co/Agnes-AI/Agnes-3.0-Flash) (Apache-2.0) — loads as a stock `qwen3_5` model with no custom code.

	- Affine 4-bit, group size 64 — 4.50 bits/weight, 17 GB
	- 262,144-token context, thinking on/off via the original chat template
	- Text only: MTP head and vision tower not included

	## What changed

	Converted from the original Agnes format to standard Qwen3.5 architecture:

	- Folded parallel FFN into main MLP via concatenation (intermediate_size: 19456)
	- Renamed `delta_attn` → `linear_attn`, `global_attn` → `self_attn`
	- Converted one-centered RMSNorm to standard format
	- Cast bf16 → fp16 for serialization compatibility
	- Stripped MTP weights (prevents double-conversion in mlx_lm)

	## Usage

	```bash
	pip install mlx-lm
	mlx_lm.generate --model hermitdave/Agnes-3.0-Flash-MLX-4bit --prompt "Hello" --max-tokens 200
	```

	Drop the folder under `~/.lmstudio/models/hermitdave/` in LM Studio and it appears as a `qwen3_5` model.

	## Speculative decoding with MTP drafter

	A companion MTP drafter is available for speculative decoding (up to 2× faster generation):

	```bash
	pip install mlx-vlm
	python -m mlx_vlm.server \
	--model hermitdave/Agnes-3.0-Flash-MLX-4bit \
	--draft-model hermitdave/Agnes-3.0-Flash-MTP-drafter
	```

	Or with the Python API:

	```python
	from mlx_lm import load
	from mlx_vlm.speculative.drafters.qwen3_5_mtp.config import Qwen3_5MTPConfig
	from mlx_vlm.speculative.drafters.qwen3_5_mtp.qwen3_5_mtp import Qwen3_5MTPDraftModel
	import json, mlx.core as mx, safetensors.torch

	base_model, tokenizer = load("hermitdave/Agnes-3.0-Flash-MLX-4bit")
	config = Qwen3_5MTPConfig.from_dict(json.load(open("path/to/Agnes-3.0-Flash-MTP-drafter/config.json")))
	mtp = Qwen3_5MTPDraftModel(config)
	weights = safetensors.torch.load_file("path/to/Agnes-3.0-Flash-MTP-drafter/model.safetensors")
	mtp.load_weights([(k, mx.array(v)) for k, v in weights.items()])
	mtp.bind(base_model)
	```

	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.

	Note: oMLX does not yet support MTP for text-only models. Use `mlx_vlm.server` or the Python API.
	## Attribution

	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.