Instructions to use lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF
# Run inference directly in the terminal:
llama cli -hf lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF
# Run inference directly in the terminal:
llama cli -hf lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF
# Run inference directly in the terminal:
./llama-cli -hf lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF

Use Docker

docker model run hf.co/lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF

LM Studio
Jan

vLLM

How to use lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF

Ollama
How to use lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF with Ollama:
```
ollama run hf.co/lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF
```

Unsloth Studio

How to use lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF to start chatting

How to use lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF with Docker Model Runner:
```
docker model run hf.co/lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF
```

Lemonade

How to use lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-ROCmFPX-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF

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 lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF

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 "lmcoleman/Qwen3.6-35B-A3B-ROCmFPX-GGUF" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.6-35B-A3B-ROCmFPX-GGUF / README.md

lmcoleman

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metadata

license: apache-2.0
library_name: llama.cpp
base_model:
  - Qwen/Qwen3.6-35B-A3B
base_model_relation: quantized
pipeline_tag: text-generation
quantized_by: ROCmFPX
language:
  - en
tags:
  - gguf
  - rocm
  - amd
  - strix-halo
  - gfx1151
  - rocmfpx
  - quantized
  - magicquant

Qwen3.6-35B-A3B-ROCmFPX-GGUF

⚠️ These files do NOT load on standard llama.cpp

They use AMD-native *_ROCMFPX tensor types from the experimental ciru-ai/ROCmFPX llama.cpp fork (build from source).

Derivative of Qwen3.6-35B-A3B, quantized using MagicQuant hybrid evolutionary per-tensor search and quantized to AMD-native ROCmFPX formats (fork-only) tuned for Strix Halo (gfx1151).

Base Model

This is a derivative of Qwen3.6-35B-A3B. All credit for the base model architecture and weights goes to the original authors. The base model's license applies to this derivative.

Quantization Method

Quantized using MagicQuant hybrid evolutionary per-tensor quantization, based on the methodology by magiccodingman:

Tensors are classified into sensitivity groups (Embeddings, Head, Query, Key, Output, FFN Up/Down, MoE Experts, Router)
An evolutionary search finds the optimal quantization type per group, balancing size vs. perplexity
Q4/Q5/Q6 tier targets are produced with different size-quality tradeoffs
Small-row tensors and sensitivity-critical layers (embeddings, output head, router) are kept at F32/F16/BF16
This is NOT a uniform quantization -- each tensor group gets its own optimal type

ROCmFPX (AMD-native, fork-only)

These GGUFs use AMD-native quantization schemes from the experimental ciru-ai/ROCmFPX llama.cpp fork, tuned for and benchmarked on AMD Strix Halo (Radeon 8060S iGPU, gfx1151, unified memory):

ROCmFP3/4/6/8 tensor types with straight and "agent" presets (agent presets keep tool-calling / JSON-structured output reliable at low bit-widths)
Files load only on the fork -- it is an experimental upstream research build, so build from the pinned commit that produced these files (the default branch may have moved on since):

git clone https://github.com/ciru-ai/ROCmFPX.git ROCmFPX
cd ROCmFPX
git checkout 68f23f34c12d7e61177a034b0d8d3fea2129565e
# then build per the fork's own README

GGUF Files

File	Size	Quant
Qwen3.6-35B-A3B-ROCMFPX-MQ-Q4.gguf	22.7 GB	MagicQuant Q4 layout in ROCmFPX types (hybrid, fork-only)

Usage

Requires a from-source build of the ROCmFPX fork (stock llama.cpp, LM Studio, and Ollama cannot load these files):

# Interactive chat (--jinja uses the model's embedded chat template)
llama-cli -m Qwen3.6-35B-A3B-ROCMFPX-MQ-Q4.gguf -c 8192 --jinja -cnv

# Server mode
llama-server -m Qwen3.6-35B-A3B-ROCMFPX-MQ-Q4.gguf -c 8192 --port 8080 -ngl 99 -fa on --jinja

Serving: MTP Speculative Decoding

This model includes MTP ("nextn") draft tensors, enabling self-speculative decoding -- measured ~1.6-1.9x faster generation with a ~95% first-token accept rate (no separate draft model needed; it drafts from itself):

llama-server -m Qwen3.6-35B-A3B-ROCMFPX-MQ-Q4.gguf -c 8192 --port 8080 --host 127.0.0.1 -ngl 99 -md Qwen3.6-35B-A3B-ROCMFPX-MQ-Q4.gguf --spec-type draft-mtp -ctk q8_0 -ctv q8_0 -fa on

Memory cost: MTP needs its own draft context alongside the main context, so serving with it uses roughly 2x the model's memory compared to serving without -md/--spec-type draft-mtp.

Caveats

The base model's license (apache-2.0) applies to all derivative files
Fork-only files: stock llama.cpp, LM Studio, and Ollama cannot load these -- build ciru-ai/ROCmFPX from source
Quantization reduces precision -- verify outputs for your specific use case
The hybrid quantization assigns different precision to different tensor groups, which means quality characteristics may differ from uniform quantizations

Limitations

Quantized models may exhibit subtle differences from the full-precision fine-tune
This model inherits any limitations and biases present in the base model

Generated with MagicQuant