---
license: apache-2.0
language:
  - en
base_model: Qwen/Qwen3-1.7B
tags:
  - qwen3
  - gguf
  - layer-surgery
  - small-language-model
  - pruned
  - optimized
  - thinking
  - text-generation
model_name: Qwen3-g023-tiny-v1
pipeline_tag: text-generation
library_name: llama.cpp
quantized_by: g023
---

# Qwen3-g023-tiny-v1 — GGUF

**A surgically optimized 27-layer Qwen3 variant that outperforms the original 28-layer model.**

Created by selectively deleting a harmful layer and swapping adjacent layers for improved information flow. Scores **92.9/100** with **100% factual accuracy** — a 5.1-point improvement over the original Qwen3-1.7B baseline (87.8/100).

## Available Quantizations

| Quantization | Bits/Weight | Description | Download |
|:---:|:---:|:---|:---:|
| **Q8_0** | 8.00 | Highest quality, virtually lossless (USE THIS ONE) | [Qwen3-g023-tiny-v1-Q8_0.gguf](./Qwen3-g023-tiny-v1-Q8_0.gguf) |
| **Q6_K** | 6.57 | Excellent quality, good compression | [Qwen3-g023-tiny-v1-Q6_K.gguf](./Qwen3-g023-tiny-v1-Q6_K.gguf) |
| **Q4_K_M** | 4.85 | Good balance of quality and size | [Qwen3-g023-tiny-v1-Q4_K_M.gguf](./Qwen3-g023-tiny-v1-Q4_K_M.gguf) |
| **Q3_K_M** | 3.91 | High compression, moderate quality loss | [Qwen3-g023-tiny-v1-Q3_K_M.gguf](./Qwen3-g023-tiny-v1-Q3_K_M.gguf) |
| **Q2_K** | 3.35 | Maximum compression, significant quality loss | [Qwen3-g023-tiny-v1-Q2_K.gguf](./Qwen3-g023-tiny-v1-Q2_K.gguf) |

## Model Details

| Parameter | Value |
|:---|:---|
| Architecture | Qwen3ForCausalLM |
| Layers | **27** (28 original − 1 deleted) |
| 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 | **~1.67B** |
| Precision (source) | bfloat16 |

## Surgery Operations

This model was created by applying two surgical operations to [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B):

1. **Delete layer 10** — Layer 10 was identified as harmful to model quality. Removing it improved the overall score from 85.9 to 91.4.
2. **Swap layers 11 ↔ 12** (post-deletion indices) — Swapping these adjacent attention blocks optimized information flow between the model's middle layers, further improving the score to 92.9.

### Key Findings

- **Smaller is better**: The 27-layer model outperforms both the 28-layer original and various 29–30 layer expanded models.
- **Layer 10 is actively harmful**: Removing it alone yields a +3.6 point improvement.
- **Operations compound selectively**: Deletion + swap works, but deletion + duplication degrades quality.

## Benchmark Results

| Metric | Original (28L) | **v1 (27L)** | Δ |
|:---|:---:|:---:|:---:|
| **Overall Score** | 87.8 / 100 | **92.9 / 100** | **+5.1** |
| **Factual Accuracy** | 15 / 17 (88%) | **17 / 17 (100%)** | **+12%** |
| Avg Perplexity | — | 15.70 | — |
| 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-v1-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-v1 -f Modelfile
ollama run qwen3-tiny-v1
```

### With llama.cpp

```bash
# Interactive chat
llama-cli -m Qwen3-g023-tiny-v1-Q8_0.gguf \
  --chat-template chatml -cnv

# Thinking mode
llama-cli -m Qwen3-g023-tiny-v1-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-v1-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-v1-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 | ~2.0 GB | ~2.0 GB |
| Q6_K | ~1.7 GB | ~1.7 GB |
| Q4_K_M | ~1.3 GB | ~1.3 GB |
| Q3_K_M | ~1.1 GB | ~1.1 GB |
| Q2_K | ~0.9 GB | ~0.9 GB |

## v1 vs v2

This model (v1) is the **Phase 1 champion**, focused on surgical precision with minimal operations.

| | v1 (this model) | [v2](https://huggingface.co/g023/Qwen3-g023-tiny-v2-GGUF) |
|:---|:---:|:---:|
| 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 development process:

1. **Phase 1**: Exhaustive search across 150+ configurations testing deletion, duplication, swapping, interpolation, and combined operations
2. **Evaluation**: Each configuration was scored on factual accuracy (17 questions), completion coherence, perplexity, repetition ratio, and thinking mode functionality
3. **Selection**: The champion was selected based on overall score, with factual accuracy as a tiebreaker

The surgery framework is available in the [source repository](https://huggingface.co/g023/Qwen3-g023-tiny-v1-GGUF).

## 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.