Instructions to use hermitdave/Agnes-3.0-Flash-MLX-8bit-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-8bit-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-8bit-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-8bit-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-8bit-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-8bit-MTP" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use hermitdave/Agnes-3.0-Flash-MLX-8bit-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-8bit-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-8bit-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-8bit-MTP", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use hermitdave/Agnes-3.0-Flash-MLX-8bit-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-8bit-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-8bit-MTP
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use hermitdave/Agnes-3.0-Flash-MLX-8bit-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-8bit-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-8bit-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"
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-8bit-MTPRun Hermes
hermesAgnes-3.0-Flash — MLX 8-bit + MTP
8-bit MLX quantization of Agnes-AI/Agnes-3.0-Flash (Apache-2.0), grafted with Qwen3.8's vision tower and Agnes's native MTP head for enhanced inference.
- Affine 8-bit, group size 64 — 8.50 bits/weight, ~37.8GB (8 shards)
- 262,144-token context, thinking on/off via the original chat template
- MTP head grafted: Agnes native MTP head with FFN padded 17408→19456 (zero-pad, mathematically identical), quantized to 4-bit
- Vision tower grafted from Qwen3.8 (333 bf16 tensors, byte-identical to Agnes original)
Architecture
Agnes-3.0-Flash is a 33B hybrid attention model:
- 54 recurrent (Gated DeltaNet) layers + 18 global attention layers
- GQA 6:1 (24q/4kv heads, dim 256), 3-axis RoPE, causal conv kernel 4
- Hidden size 5120, intermediate size 17408 (Agnes native)
- MTP head intermediate size 19456 (Qwen3.8 compatible, padded from 17408)
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
MTP graft:
- Padded Agnes native MTP head FFN layers (gate_proj, up_proj: +2048 rows; down_proj: +2048 cols) to match Qwen3.8's expected 19456
- Quantized MTP weights to 4-bit affine (group_size=64)
- Grafted 333 vision tensors from Qwen3.8 (byte-identical to Agnes original)
Benchmark
Measured on M3 Max 64GB, oMLX, temperature 0:
| Metric | Baseline (no MTP) | With MTP | Speedup |
|---|---|---|---|
| Throughput | 15.0 tok/s | 21.7 tok/s | 1.44× |
| Acceptance rate | — | 85.7% | — |
| Tokens per cycle | — | 2.46 | — |
Usage
pip install mlx-lm
mlx_lm.generate --model hermitdave/Agnes-3.0-Flash-MLX-8bit-MTP --prompt "Hello" --max-tokens 200
Or with oMLX: place under ~/.omlx/models/hermitdave/ and enable mtp_enabled + vlm_mtp_enabled in model settings.
Attribution
This conversion was produced by Hermes Agent (Nous Research). MTP graft methodology: pad Agnes native MTP head FFN to Qwen3.8 size (19456), quantize to 4-bit, graft Qwen3.8 vision tower.
- Downloads last month
- 223
8-bit
Model tree for hermitdave/Agnes-3.0-Flash-MLX-8bit-MTP
Base model
Agnes-AI/Agnes-3.0-Flash
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-8bit-MTP"