Instructions to use TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use TheStageAI/Qwen3.5-2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheStageAI/Qwen3.5-2B-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": "TheStageAI/Qwen3.5-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M

Ollama
How to use TheStageAI/Qwen3.5-2B-GGUF with Ollama:
```
ollama run hf.co/TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M
```

Unsloth Studio

How to use TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TheStageAI/Qwen3.5-2B-GGUF to start chatting

How to use TheStageAI/Qwen3.5-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TheStageAI/Qwen3.5-2B-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": "TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use TheStageAI/Qwen3.5-2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M

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 "TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M" \
  --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"

Docker Model Runner
How to use TheStageAI/Qwen3.5-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M
```

Lemonade

How to use TheStageAI/Qwen3.5-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-2B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-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 TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Qwen3.5-2B-GGUF / README.md

michellemoorre

Add private release-card draft

accf1d9 verified 19 days ago

4.55 kB

license: apache-2.0
base_model:
  - Qwen/Qwen3.5-2B
base_model_relation: quantized
library_name: llama.cpp
pipeline_tag: text-generation
tags:
  - gguf
  - llama.cpp
  - quantization
  - mixed-precision

Qwen3.5 2B — TheStageAI GGUF

Four text-only GGUF checkpoints from 0.738 GB to 2.013 GB, evaluated on IFEval and MMLU-Pro where a complete release score is available.

Qwen3.5 0.8B · Qwen3.5 2B · Qwen3.5 4B · Qwen3.5 9B · Gemma 4 E2B IT · Gemma 4 E4B IT · Gemma 4 12B IT

Choose a file

Variant	Hub class	File	Size	Whole-file BPW	IFEval P / I (%)	MMLU-Pro (%)	Use
XS	`Q3_K_S`	`Qwen3.5-2B-XS-TS-Q3_K_S.gguf`	0.738 GB	3.140	52.68 / 64.15	—	Minimum size
S	`Q4_K_S`	`Qwen3.5-2B-S-TS-Q4_K_S.gguf`	0.967 GB	4.109	63.22 / 73.38	—	Compact
M	`Q4_K_M`	`Qwen3.5-2B-M-TS-Q4_K_M.gguf`	1.073 GB	4.562	66.54 / 75.54	—	Recommended · Uniform Q4
L	`Q8_0`	`Qwen3.5-2B-L-TS-Q8_0.gguf`	2.013 GB	8.558	65.80 / 74.94	—	Uniform Q8

Hub class versus file type: XS and S are TheStage mixed-precision schedules. Their Hub labels describe the whole-file size class for discoverability; they are not stock Q3_K_S, Q3_K_L, or Q4_K_S conversions. M and L use the actual MOSTLY_Q4_K_M and MOSTLY_Q8_0 GGUF file types. Exact tensor-type counts and SHA-256 digests are in release-manifest.json.

IFEval is shown as prompt-strict / instruction-strict. The BF16 reference scored 65.43 / 74.70 on IFEval. MMLU-Pro is not reported for the BF16 reference in this release.

Quickstart

Use a current llama.cpp build. The command below selects the recommended M file explicitly through its Hub quant label:

llama-cli -hf TheStageAI/Qwen3.5-2B-GGUF:Q4_K_M

Evaluation

IFEval: 541 prompts, native chat template, enable_thinking=false, temperature 0.
MMLU-Pro: 12,032 questions for complete rows, native chat template, enable_thinking=true, temperature 1, top-p 0.95.
A dash means not reported, not zero and not a score reconstructed from partial subjects.

These protocols intentionally exercise different operating modes. IFEval measures non-thinking instruction and format adherence; MMLU-Pro includes long sampled reasoning.

What this release is

This release starts from Qwen/Qwen3.5-2B at revision 15852e8c16360a2fea060d615a32b45270f8a8fc and applies TheStageAI's scale-tuned deployment compression pipeline.

XS: minimum-size adaptive mixed precision.
S: compact adaptive mixed precision.
M: uniform Q4 operating point.
L: uniform Q8 operating point.

The four products are operating points, not a promise that benchmark scores increase monotonically with file size. The recommended row is selected separately for each base model from the release evaluations.

Limitations

The files in this repository contain the language-model GGUF. Multimodal projector files are not included.
Small score reversals between BF16 and quantized rows should be read as evaluation variation, not as a claim that quantization improves the base model.
For XS, use enable_thinking=false. A headline thinking-mode MMLU-Pro score is not reported when long generations do not produce a stable product metric.
MMLU-Pro is not reported for this model in this release; diagnostic partial subjects are deliberately not aggregated.

Provenance

The exact base revision, file sizes, GGUF file types, whole-file BPW, SHA-256 digests, held-out KL values, and evaluation IDs are recorded in release-manifest.json. Release filenames differ from the internal artifact paths; the payload bytes must match those digests exactly.

License

The model weights are released under the upstream model's apache-2.0 license. llama.cpp and any surrounding runtime code retain their own licenses.