Instructions to use sh111111111111111/Qwen3.5-4B-BitClass3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sh111111111111111/Qwen3.5-4B-BitClass3-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sh111111111111111/Qwen3.5-4B-BitClass3-GGUF",
	filename="Qwen3.5-4B-Q3_K_S.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 sh111111111111111/Qwen3.5-4B-BitClass3-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 sh111111111111111/Qwen3.5-4B-BitClass3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sh111111111111111/Qwen3.5-4B-BitClass3-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 sh111111111111111/Qwen3.5-4B-BitClass3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sh111111111111111/Qwen3.5-4B-BitClass3-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 sh111111111111111/Qwen3.5-4B-BitClass3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sh111111111111111/Qwen3.5-4B-BitClass3-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 sh111111111111111/Qwen3.5-4B-BitClass3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sh111111111111111/Qwen3.5-4B-BitClass3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sh111111111111111/Qwen3.5-4B-BitClass3-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use sh111111111111111/Qwen3.5-4B-BitClass3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sh111111111111111/Qwen3.5-4B-BitClass3-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": "sh111111111111111/Qwen3.5-4B-BitClass3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sh111111111111111/Qwen3.5-4B-BitClass3-GGUF:Q4_K_M

Ollama
How to use sh111111111111111/Qwen3.5-4B-BitClass3-GGUF with Ollama:
```
ollama run hf.co/sh111111111111111/Qwen3.5-4B-BitClass3-GGUF:Q4_K_M
```

Unsloth Studio

How to use sh111111111111111/Qwen3.5-4B-BitClass3-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 sh111111111111111/Qwen3.5-4B-BitClass3-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 sh111111111111111/Qwen3.5-4B-BitClass3-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sh111111111111111/Qwen3.5-4B-BitClass3-GGUF to start chatting

How to use sh111111111111111/Qwen3.5-4B-BitClass3-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sh111111111111111/Qwen3.5-4B-BitClass3-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": "sh111111111111111/Qwen3.5-4B-BitClass3-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sh111111111111111/Qwen3.5-4B-BitClass3-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 sh111111111111111/Qwen3.5-4B-BitClass3-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 sh111111111111111/Qwen3.5-4B-BitClass3-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use sh111111111111111/Qwen3.5-4B-BitClass3-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sh111111111111111/Qwen3.5-4B-BitClass3-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 "sh111111111111111/Qwen3.5-4B-BitClass3-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 sh111111111111111/Qwen3.5-4B-BitClass3-GGUF with Docker Model Runner:
```
docker model run hf.co/sh111111111111111/Qwen3.5-4B-BitClass3-GGUF:Q4_K_M
```

Lemonade

How to use sh111111111111111/Qwen3.5-4B-BitClass3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sh111111111111111/Qwen3.5-4B-BitClass3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-4B-BitClass3-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.5-4B — BitClass3 Mixed-Precision GGUF

Mixed-precision GGUF quantizations of Qwen3.5-4B — the first BitClass release of this model. BitClass3 keeps the Hessian-sensitivity front-end to set each level's bit budget, but hands the per-tensor allocation to an error-minimizing solver (built on llama.cpp's --target-bpw) that distributes bits across every tensor — including the hybrid DeltaNet/SSM tensors — to minimize imatrix-weighted quantization error at the target size.

Available Quantizations

KLD vs the BF16 source is the primary quality metric (mean and the robust 99.9th percentile); wikitext-2 perplexity is reported alongside.

File	BPW	Size	wiki PPL ↓	KL-mean ↓	KL-99.9% ↓	Use Case
`Qwen3.5-4B-Q8_0.gguf`	8.5	4.48 GB	8.638	0.0028	0.073	Near-lossless reference
`Qwen3.5-4B-Q6_K.gguf`	6.2	3.25 GB	8.767	0.0125	0.396	High quality
`Qwen3.5-4B-Q5_K_M.gguf`	5.1	2.71 GB	9.176	0.0255	0.803	Balanced quality and size
`Qwen3.5-4B-Q4_K_M.gguf`	5.0	2.61 GB	9.246	0.0292	0.952	Best quality-to-size ratio
`Qwen3.5-4B-Q3_K_S.gguf`	3.8	1.98 GB	9.358	0.0978	3.583	Maximum compression

Recommended: Q4_K_M — KL-mean 0.029 at 2.61 GB.

How It Compares

Same harness, same metrics, against a fixed per-suffix LP-recipe baseline at matched BPW (within 0.5%) — the head-to-head that motivated BitClass3's allocator:

Level	LP recipe KL-mean	BitClass3 KL-mean
Q3_K_S	0.1525	0.0978
Q4_K_M	0.0352	0.0292
Q5_K_M	0.0258	0.0255

The allocator wins KL divergence at every level — largest at aggressive quantization (Q3_K_S: −36% KL-mean) — and our internal worst-token measurements improve at every level as well. A key reason: it allocates bits to the hybrid DeltaNet tensors (attn_qkv, attn_gate, ssm_*) that a standard 7-suffix recipe leaves at the base type — and our tensor-health scan shows those exact tensors are the statistical outliers of this architecture.

Key Sensitivity Findings (Qwen3.5-4B)

blk.3 (early layer) is most sensitive — the same early-layer pattern as Qwen3.5-9B, and the opposite of dense Qwen3-4B-Instruct (blk.34). The hybrid Qwen3.5 family concentrates sensitivity early.
Attention K projections are consistently ≥ V in sensitivity.
DeltaNet/SSM tensors are distribution outliers (high kurtosis ssm_conv1d, shifted ssm_alpha/beta/out, attn_qkv, attn_gate vs same-role peers) — covering them in the allocation matters; ssm_conv1d itself is kept at F32 by llama.cpp.

How It Works

Hessian sensitivity — compute H_diag = mean(X²) per layer on calibration data; this sets each level's overall bit budget.
Error-minimizing per-tensor allocation — an imatrix-weighted solver (llama.cpp --target-bpw) assigns a quant type to every tensor to minimize total quantization error at the target BPW, covering attention, FFN, and the hybrid DeltaNet/SSM tensors.
imatrix — importance matrix computed over wikitext guides the per-tensor error.
GGUF export — produced with stock llama-quantize.

Usage

hf download sh111111111111111/Qwen3.5-4B-BitClass3-GGUF \
    Qwen3.5-4B-Q4_K_M.gguf --local-dir .

llama-cli    -m Qwen3.5-4B-Q4_K_M.gguf -cnv
llama-server -m Qwen3.5-4B-Q4_K_M.gguf --port 8080

Note: Qwen3.5 GGUFs are not currently runnable in Ollama (vision/mmproj handling is not yet supported there); use llama.cpp or LM Studio.

Benchmark Details

NVIDIA GB10 ATOM (128 GB unified memory, aarch64). llama.cpp with --target-bpw (PR #15550). KLD via llama-perplexity --kl-divergence against BF16-source logits over wikitext-2 (mean / median / 99.9th percentile reported; the single-token KL-max is omitted as an unstable order statistic). wikitext-2 PPL via llama-perplexity -c 2048. Downstream (HellaSwag / WinoGrande / ARC / MMLU) tracked internally.

Disclaimer

Independent project. Not affiliated with or endorsed by Qwen, Unsloth, ByteShape, Bartowski, or llama.cpp. Competitor figures are from our own benchmark harness and may differ from those projects' self-reported numbers; competitor file sizes reflect the revision we tested and may since have changed.

License

Apache 2.0, inherited from Qwen3.5-4B.

Downloads last month: 295

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for sh111111111111111/Qwen3.5-4B-BitClass3-GGUF

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Quantized

(280)

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