Instructions to use IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-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 IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-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 IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1
# Run inference directly in the terminal:
llama cli -hf IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1
# Run inference directly in the terminal:
llama cli -hf IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1

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 IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1
# Run inference directly in the terminal:
./llama-cli -hf IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1

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 IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1

Use Docker

docker model run hf.co/IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1

LM Studio
Jan
Ollama
How to use IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF with Ollama:
```
ollama run hf.co/IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1
```
Unsloth Desktop

How to use IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1

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": "IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF with Docker Model Runner:
```
docker model run hf.co/IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1
```

Lemonade

How to use IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1

Run and chat with the model

lemonade run user.Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF-Q4_1

List all available models

lemonade list

Hermes Agent

How to use IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-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 IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1

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 IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1

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 "IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF:Q4_1" \
  --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"

This is the Q4_1 quant optimized for older cards from Bartowski patched with the missing MTP tensors.
The modified conversion script from user buzz can be found here
For more information see the PR or the /r/locallama discussion

NOTE: This file only work with the am17an:mtp-clean branch mentioned in the above PR or other MTP supported branches until official support is merged.

The 27B dense version can be found here
Dual Mi50@150W with -sm tensor and -ngram mod performance compressing a 120K token context:

prompt eval time = 168364.79 ms / 121534 tokens ( 1.39 ms per token, 721.85 tokens per second)
eval time = 7194.32 ms / 410 tokens ( 17.55 ms per token, 56.99 tokens per second)
total time = 175559.11 ms / 121944 tokens
slot release: id 2 | task 12770 | stop processing: n_tokens = 121943, truncated = 0

Downloads last month: 60

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

4-bit

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Model tree for IHaveNoClueAndIMustPost/Qwen_Qwen3.6-35B-A3B-MTP-Q4_1-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

bartowski/Qwen_Qwen3.6-35B-A3B-GGUF

Quantized

(1)

this model