Instructions to use schoggie/Qwen3.6-35B-A3B-java-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use schoggie/Qwen3.6-35B-A3B-java-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="schoggie/Qwen3.6-35B-A3B-java-v1-GGUF",
	filename="qwen36-a3b-java-v1.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use schoggie/Qwen3.6-35B-A3B-java-v1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M

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 schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M

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 schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use schoggie/Qwen3.6-35B-A3B-java-v1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "schoggie/Qwen3.6-35B-A3B-java-v1-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": "schoggie/Qwen3.6-35B-A3B-java-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M

Ollama
How to use schoggie/Qwen3.6-35B-A3B-java-v1-GGUF with Ollama:
```
ollama run hf.co/schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M
```

Unsloth Studio

How to use schoggie/Qwen3.6-35B-A3B-java-v1-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 schoggie/Qwen3.6-35B-A3B-java-v1-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 schoggie/Qwen3.6-35B-A3B-java-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for schoggie/Qwen3.6-35B-A3B-java-v1-GGUF to start chatting

How to use schoggie/Qwen3.6-35B-A3B-java-v1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use schoggie/Qwen3.6-35B-A3B-java-v1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M

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 schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use schoggie/Qwen3.6-35B-A3B-java-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M
```

Lemonade

How to use schoggie/Qwen3.6-35B-A3B-java-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull schoggie/Qwen3.6-35B-A3B-java-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-java-v1-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.6-35B-A3B-java-v1 — GGUF quants

GGUF quantizations of schoggie/Qwen3.6-35B-A3B-java-v1 — a QLoRA fine-tune of Qwen/Qwen3.6-35B-A3B for agentic Java coding and long-context recall.

See the parent model card for training details, evaluation, and intended use.

Quants

File	Size	Recommended hardware	Notes
`qwen36-a3b-java-v1.BF16.gguf`	65 GB	re-quantization source	Lossless reference, use to make new quant types
`qwen36-a3b-java-v1.Q8_0.gguf`	35 GB	48 GB+ GPU	Near-lossless
`qwen36-a3b-java-v1.Q6_K.gguf`	27 GB	2× 16 GB GPU (production deploy)	Recommended — used by maintainer at 200 K context on dual V100
`qwen36-a3b-java-v1.Q5_K_M.gguf`	24 GB	32 GB GPU
`qwen36-a3b-java-v1.Q4_K_M.gguf`	20 GB	24 GB single GPU	imatrix-tuned
`qwen36-a3b-java-v1.Q3_K_M.gguf`	16 GB	20 GB GPU	imatrix-tuned
`qwen36-a3b-java-v1.IQ2_M.gguf`	11 GB	16 GB consumer GPU	imatrix-tuned, useful floor

The qwen36-a3b-java-v1.imatrix.dat (192 MB) and calibration_java.txt (Java-domain calibration corpus used to generate the importance matrix) are included for reproducibility / re-quantization with different bit widths.

Usage

llama.cpp server

llama-server -m qwen36-a3b-java-v1.Q6_K.gguf \
  --host 0.0.0.0 --port 8080 \
  -ngl 99 -c 32768 --jinja -fa on -fit off

Ollama

ollama create qwen36-a3b-java-v1 -f Modelfile   # FROM ./qwen36-a3b-java-v1.Q6_K.gguf
ollama run qwen36-a3b-java-v1

LM Studio

Drop the .gguf into your models directory and load via the UI.

Note on llama.cpp loader. Stock upstream llama.cpp has known loader bugs on the Qwen3.6-A3B GGUF metadata path. Use the unsloth-maintained fork until the upstream patch lands.

License

Inherits the Qwen Research License from the base model.

Downloads last month: 677

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for schoggie/Qwen3.6-35B-A3B-java-v1-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Adapter

schoggie/Qwen3.6-35B-A3B-java-v1

Quantized

(1)

this model