Instructions to use g023/qwen3-tiny-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use g023/qwen3-tiny-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="g023/qwen3-tiny-v2",
	filename="Qwen3-g023-tiny-v2-Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use g023/qwen3-tiny-v2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf g023/qwen3-tiny-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf g023/qwen3-tiny-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf g023/qwen3-tiny-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf g023/qwen3-tiny-v2: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 g023/qwen3-tiny-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf g023/qwen3-tiny-v2: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 g023/qwen3-tiny-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf g023/qwen3-tiny-v2:Q4_K_M

Use Docker

docker model run hf.co/g023/qwen3-tiny-v2:Q4_K_M

LM Studio
Jan

vLLM

How to use g023/qwen3-tiny-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "g023/qwen3-tiny-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "g023/qwen3-tiny-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/g023/qwen3-tiny-v2:Q4_K_M

Ollama
How to use g023/qwen3-tiny-v2 with Ollama:
```
ollama run hf.co/g023/qwen3-tiny-v2:Q4_K_M
```

Unsloth Studio new

How to use g023/qwen3-tiny-v2 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 g023/qwen3-tiny-v2 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 g023/qwen3-tiny-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for g023/qwen3-tiny-v2 to start chatting

Pi new

How to use g023/qwen3-tiny-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf g023/qwen3-tiny-v2: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": "g023/qwen3-tiny-v2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use g023/qwen3-tiny-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf g023/qwen3-tiny-v2: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 g023/qwen3-tiny-v2:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use g023/qwen3-tiny-v2 with Docker Model Runner:
```
docker model run hf.co/g023/qwen3-tiny-v2:Q4_K_M
```

Lemonade

How to use g023/qwen3-tiny-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull g023/qwen3-tiny-v2:Q4_K_M

Run and chat with the model

lemonade run user.qwen3-tiny-v2-Q4_K_M

List all available models

lemonade list

Qwen3-g023-tiny-v2 — GGUF

An advanced 30-layer Qwen3 variant using swap, interpolation, and skip-bridge surgery.

Created through innovative layer surgery combining multi-swap, interpolation, and bridge (skip connection) techniques. Scores 94.3/100 — a 6.5-point improvement over the original Qwen3-1.7B baseline (87.8/100) and the highest score achieved in two phases of experimentation across ~250 configurations. (I have my own benchmarks, so results may vary if you run your own tests.)

Available Quantizations

Quantization	Bits/Weight	Description	Download
Q8_0	8.00	Highest quality, virtually lossless (USE THIS ONE)	Qwen3-g023-tiny-v2-Q8_0.gguf
Q6_K	6.57	Excellent quality, good compression	Qwen3-g023-tiny-v2-Q6_K.gguf
Q4_K_M	4.85	Good balance of quality and size	Qwen3-g023-tiny-v2-Q4_K_M.gguf
Q3_K_M	3.91	High compression, moderate quality loss	Qwen3-g023-tiny-v2-Q3_K_M.gguf
Q2_K	3.35	Maximum compression, significant quality loss	Qwen3-g023-tiny-v2-Q2_K.gguf

Model Details

Parameter	Value
Architecture	Qwen3ForCausalLM
Layers	30 (28 original + 2 from surgery)
Hidden Size	2,048
Intermediate Size	6,144
Attention Heads	16 query / 8 key-value (GQA)
Head Dimension	128
Vocabulary	151,936 tokens
Max Context	40,960 tokens
RoPE θ	1,000,000
Tied Embeddings	Yes
Total Parameters	~1.82B
Precision (source)	bfloat16

Surgery Operations

This model was created by applying three innovative surgical operations to Qwen/Qwen3-1.7B:

Multi-swap: layers 12↔13 and 16↔17 — Reorders attention blocks at two critical points in the network for improved representational flow through the mid-layers.
Interpolation: layers 20 & 22 (α=0.5) — Creates a new layer by blending the weights of layers 20 and 22 at equal proportions, producing a smoother transition in the upper layers.
Bridge (skip connection): layer 5 → after layer 20 — Copies early-layer representations (layer 5) and inserts them after layer 20, creating a skip connection that helps preserve low-level features deep in the network.

Why These Operations Work

Multi-swap corrects suboptimal layer ordering that emerged from pre-training, allowing better gradient flow through the network's critical middle section.
Interpolation creates a synthetic transition layer that smooths the representation gap between layers 20 and 22, reducing the information bottleneck.
Bridge/skip connections address the "forgetting problem" in deep networks by reintroducing early feature representations at later stages — a technique inspired by ResNet's residual connections but applied at the transformer layer level.

Benchmark Results

Metric	Original (28L)	v1 (27L)	v2 (30L)	Δ vs Original
Overall Score	87.8 / 100	92.9 / 100	94.3 / 100	+6.5
Factual Accuracy	15/17 (88%)	17/17 (100%)	16/17 (94%)	+6%
Avg Perplexity	—	15.70	15.17	—
Thinking Mode	✅	✅	✅	—
Non-Thinking Mode	✅	✅	✅	—

Evaluated using a comprehensive test suite with 17 factual questions, 2 completion coherence tests, perplexity measurements, repetition analysis, and thinking/non-thinking mode verification.

Features

Thinking mode: Full <think> / </think> reasoning support — toggle via enable_thinking parameter
Non-thinking mode: Direct responses without chain-of-thought overhead
Tool calling: Full function/tool calling support
System prompts: Standard system message support
Chat template: Qwen3 ChatML template embedded in the GGUF

Usage

With Ollama

# Download the GGUF and create from Modelfile
cat > Modelfile << 'EOF'
FROM ./Qwen3-g023-tiny-v2-Q8_0.gguf

PARAMETER temperature 1.0
PARAMETER top_p 0.95
PARAMETER top_k 45
PARAMETER min_p 0.1
PARAMETER num_ctx 40000
PARAMETER mirostat 2
PARAMETER mirostat_tau 5.0
PARAMETER mirostat_eta 0.1
PARAMETER repeat_last_n 16384
PARAMETER repeat_penalty 1.1
PARAMETER presence_penalty 0.5
PARAMETER frequency_penalty 1.0

TEMPLATE """{{- if .System }}
<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}
{{- range .Messages }}
{{- if eq .Role "user" }}
<|im_start|>user
{{ .Content }}<|im_end|>
{{- else if eq .Role "assistant" }}
<|im_start|>assistant
{{ .Content }}<|im_end|>
{{- end }}
{{- end }}
<|im_start|>assistant
"""
SYSTEM "You are a helpful assistant."
EOF

ollama create qwen3-tiny-v2 -f Modelfile
ollama run qwen3-tiny-v2

With llama.cpp

# Interactive chat
llama-cli -m Qwen3-g023-tiny-v2-Q8_0.gguf \
  --chat-template chatml -cnv

# Thinking mode
llama-cli -m Qwen3-g023-tiny-v2-Q8_0.gguf \
  -p "<|im_start|>user\nExplain quantum computing<|im_end|>\n<|im_start|>assistant\n<think>\n" \
  -n 512

# Non-thinking mode
llama-cli -m Qwen3-g023-tiny-v2-Q8_0.gguf \
  -p "<|im_start|>user\n/no_think What is 2+2?<|im_end|>\n<|im_start|>assistant\n" \
  -n 128

With Python (llama-cpp-python)

from llama_cpp import Llama

model = Llama("Qwen3-g023-tiny-v2-Q8_0.gguf", n_ctx=4096)
response = model.create_chat_completion(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"},
    ],
    temperature=0.6,
)
print(response["choices"][0]["message"]["content"])

System Requirements

Quantization	RAM (CPU)	VRAM (GPU)
Q8_0	~2.2 GB	~2.2 GB
Q6_K	~1.8 GB	~1.8 GB
Q4_K_M	~1.4 GB	~1.4 GB
Q3_K_M	~1.2 GB	~1.2 GB
Q2_K	~1.0 GB	~1.0 GB

v1 vs v2

This model (v2) is the Phase 2 champion, using advanced multi-operation surgery for the highest overall score.

	v1	v2 (this model)
Layers	27	30
Parameters	~1.67B	~1.82B
Operations	del + swap	swap + interpolate + bridge
Score	92.9 / 100	94.3 / 100
Factual	100% (17/17)	94% (16/17)
Perplexity	15.70	15.17
Use Case	Max factual accuracy	Max overall score

v1 is recommended when factual accuracy is paramount (100% vs 94%). v2 is recommended when overall quality matters more (94.3 vs 92.9).

Methodology

Layer surgery was performed through a systematic, test-driven process across two phases:

Phase 1 (~150 configs): Exhaustive search across deletion, duplication, swapping, interpolation, and combined operations → champion: del_10 + swap_11↔12 (v1)
Phase 2 (~95 configs): Advanced techniques including tripling, multi-swap, layer reversal, cycling, weight scaling, layer merging, skip bridges, and synthesis → champion: this model (v2)
Evaluation: Each configuration scored on factual accuracy (17 questions), completion coherence, perplexity, repetition ratio, and thinking mode functionality

Phase 2 Leaderboard (Top 5)

Rank	Configuration	Score	Factual	PPL
🥇	swap(12↔13,16↔17) + interp(20↔22) + bridge(5→20)	94.3	94%	15.17
🥈	swap(12↔13,16↔17) + interp(20↔22)	93.9	94%	14.74
🥉	swap(12↔13) + interp(20↔22) + bridge(5→20)	93.4	94%	15.66
4	multi-swap(12↔13,16↔17)	93.1	100%	14.90
5	Phase 1 champion (del_10 + swap_11↔12)	92.9	100%	15.70

Credits

Base model: Qwen/Qwen3-1.7B by the Qwen team at Alibaba
Quantization: llama.cpp
Surgery: g023

License

Apache 2.0 — same as the original Qwen3-1.7B model.

Downloads last month: 78

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

6-bit

8-bit

Model tree for g023/qwen3-tiny-v2

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Quantized

(276)

this model

Quantizations

1 model