Instructions to use localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF",
	filename="Qwen3.5-4B-MTP-IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama-cli -hf localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama-cli -hf localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS

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 localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS

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 localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS

Use Docker

docker model run hf.co/localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS

LM Studio
Jan
Ollama
How to use localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF with Ollama:
```
ollama run hf.co/localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS
```

Unsloth Studio

How to use localweights/Qwen3.5-4B-MTP-IQ4_XS-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 localweights/Qwen3.5-4B-MTP-IQ4_XS-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 localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF to start chatting

How to use localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS

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": "localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use localweights/Qwen3.5-4B-MTP-IQ4_XS-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 localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS

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 localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF with Docker Model Runner:
```
docker model run hf.co/localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS
```

Lemonade

How to use localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull localweights/Qwen3.5-4B-MTP-IQ4_XS-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.Qwen3.5-4B-MTP-IQ4_XS-GGUF-IQ4_XS

List all available models

lemonade list

Qwen3.5-4B-MTP-GGUF

Qwen3.5-4B (qwen35 dense hybrid arch) with NextN/MTP head preserved. Built via the patched convert_hf_to_gguf.py from patched llama.cpp build with Qwen3.5/3.6 MTP support.

Files

File	Size	Purpose
`Qwen3.5-4B-MTP-bf16.gguf`	8.1 GB	Source for further quantization. NextN tensors at `blk.32`.
`Qwen3.5-4B-MTP-IQ4_XS.gguf`	2.5 GB	Production-ready quant. Fits in tiny-council slots.

Build pipeline

python convert_hf_to_gguf.py /path/to/Qwen3.5-4B \
    --outfile Qwen3.5-4B-MTP-bf16.gguf
llama-quantize Qwen3.5-4B-MTP-bf16.gguf \
    Qwen3.5-4B-MTP-IQ4_XS.gguf IQ4_XS

Required adding the qwen35 pre-tokenizer chkhsh entry to convert_hf_to_gguf.py:1531 (vendored in the fork).

Optimal serving config (RTX 3090 Ti)

Recommended --spec-draft-n-max 2 for this model size. Larger n drops accept rate faster than throughput grows; sweet spot is shallower than the 27B/35B (which peak at n=4).

llama-server -m Qwen3.5-4B-MTP-IQ4_XS.gguf \
  -ngl 999 -fa on \
  --spec-type mtp --spec-draft-n-max 2 \
  --no-mmap \
  --ctx-size 8192 -ctk q4_0 -ctv q4_0 \
  --parallel 1 --kv-unified \
  --metrics --jinja

Performance — `--spec-draft-n-max` sweep

Measured 2026-05-06, IQ4_XS, 3090 Ti, no thinking, 200-token decode:

n	Decode tok/s	Accept rate
1	250	100%
2	290 ← peak	98%
3	280	82%
4	264	70%
5	223	54%
6	204	48%
8	188	36%

Without spec-decode (baseline): 207 tok/s. So peak MTP gives +40% vs baseline.

Metric	Value
Decode (best, n=2)	290 t/s
Speedup vs no-spec	+40%
VRAM @ 8K ctx	~2.7 GB

Tokenizer

qwen35 pre-tokenizer, 151,936 vocab. Standard chat template.

License

Apache 2.0.

Provenance

Built on Crucible: 9950X / 96 GB DDR5 / RTX 3090 Ti. Sibling: localweights/Qwen3.6-{27B,35B-A3B}-MTP-IQ4_XS-GGUF.

Downloads last month: 237

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

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