---
license: apache-2.0
language:
  - en
base_model: Qwen/Qwen3-1.7B
tags:
  - qwen3
  - gguf
  - layer-surgery
  - small-language-model
  - optimized
  - thinking
  - text-generation
  - skip-connections
  - interpolation
model_name: Qwen3-g023-tiny-v2
pipeline_tag: text-generation
library_name: llama.cpp
quantized_by: g023
---

# 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](./Qwen3-g023-tiny-v2-Q8_0.gguf) |
| **Q6_K** | 6.57 | Excellent quality, good compression | [Qwen3-g023-tiny-v2-Q6_K.gguf](./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](./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](./Qwen3-g023-tiny-v2-Q3_K_M.gguf) |
| **Q2_K** | 3.35 | Maximum compression, significant quality loss | [Qwen3-g023-tiny-v2-Q2_K.gguf](./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](https://huggingface.co/Qwen/Qwen3-1.7B):

1. **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.
2. **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.
3. **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)](https://huggingface.co/g023/Qwen3-g023-tiny-v1-GGUF) | **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

```bash
# 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

```bash
# 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)

```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](https://huggingface.co/g023/Qwen3-g023-tiny-v1-GGUF) | 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:

1. **Phase 1** (~150 configs): Exhaustive search across deletion, duplication, swapping, interpolation, and combined operations → champion: del_10 + swap_11↔12 (v1)
2. **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)
3. **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](https://huggingface.co/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.