221 lines
8.2 KiB
Markdown
221 lines
8.2 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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base_model: Qwen/Qwen3-1.7B
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tags:
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- qwen3
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- gguf
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- layer-surgery
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- small-language-model
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- optimized
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- thinking
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- text-generation
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- skip-connections
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- interpolation
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model_name: Qwen3-g023-tiny-v2
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pipeline_tag: text-generation
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library_name: llama.cpp
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quantized_by: g023
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---
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# Qwen3-g023-tiny-v2 — GGUF
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**An advanced 30-layer Qwen3 variant using swap, interpolation, and skip-bridge surgery.**
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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.)
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## Available Quantizations
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| Quantization | Bits/Weight | Description | Download |
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|:---:|:---:|:---|:---:|
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| **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) |
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| **Q6_K** | 6.57 | Excellent quality, good compression | [Qwen3-g023-tiny-v2-Q6_K.gguf](./Qwen3-g023-tiny-v2-Q6_K.gguf) |
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| **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) |
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| **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) |
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| **Q2_K** | 3.35 | Maximum compression, significant quality loss | [Qwen3-g023-tiny-v2-Q2_K.gguf](./Qwen3-g023-tiny-v2-Q2_K.gguf) |
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## Model Details
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| Parameter | Value |
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|:---|:---|
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| Architecture | Qwen3ForCausalLM |
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| Layers | **30** (28 original + 2 from surgery) |
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| Hidden Size | 2,048 |
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| Intermediate Size | 6,144 |
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| Attention Heads | 16 query / 8 key-value (GQA) |
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| Head Dimension | 128 |
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| Vocabulary | 151,936 tokens |
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| Max Context | 40,960 tokens |
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| RoPE θ | 1,000,000 |
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| Tied Embeddings | Yes |
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| Total Parameters | **~1.82B** |
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| Precision (source) | bfloat16 |
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## Surgery Operations
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This model was created by applying three innovative surgical operations to [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B):
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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.
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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.
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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.
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### Why These Operations Work
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- **Multi-swap** corrects suboptimal layer ordering that emerged from pre-training, allowing better gradient flow through the network's critical middle section.
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- **Interpolation** creates a synthetic transition layer that smooths the representation gap between layers 20 and 22, reducing the information bottleneck.
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- **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.
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## Benchmark Results
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| Metric | Original (28L) | [v1 (27L)](https://huggingface.co/g023/Qwen3-g023-tiny-v1-GGUF) | **v2 (30L)** | Δ vs Original |
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|:---|:---:|:---:|:---:|:---:|
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| **Overall Score** | 87.8 / 100 | 92.9 / 100 | **94.3 / 100** | **+6.5** |
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| **Factual Accuracy** | 15/17 (88%) | 17/17 (100%) | **16/17 (94%)** | **+6%** |
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| Avg Perplexity | — | 15.70 | **15.17** | — |
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| Thinking Mode | ✅ | ✅ | ✅ | — |
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| Non-Thinking Mode | ✅ | ✅ | ✅ | — |
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Evaluated using a comprehensive test suite with 17 factual questions, 2 completion coherence tests, perplexity measurements, repetition analysis, and thinking/non-thinking mode verification.
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## Features
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- **Thinking mode**: Full `<think>` / `</think>` reasoning support — toggle via `enable_thinking` parameter
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- **Non-thinking mode**: Direct responses without chain-of-thought overhead
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- **Tool calling**: Full function/tool calling support
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- **System prompts**: Standard system message support
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- **Chat template**: Qwen3 ChatML template embedded in the GGUF
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## Usage
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### With Ollama
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```bash
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# Download the GGUF and create from Modelfile
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cat > Modelfile << 'EOF'
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FROM ./Qwen3-g023-tiny-v2-Q8_0.gguf
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PARAMETER temperature 1.0
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PARAMETER top_p 0.95
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PARAMETER top_k 45
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PARAMETER min_p 0.1
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PARAMETER num_ctx 40000
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PARAMETER mirostat 2
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PARAMETER mirostat_tau 5.0
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PARAMETER mirostat_eta 0.1
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PARAMETER repeat_last_n 16384
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PARAMETER repeat_penalty 1.1
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PARAMETER presence_penalty 0.5
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PARAMETER frequency_penalty 1.0
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TEMPLATE """{{- if .System }}
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<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}
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{{- range .Messages }}
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{{- if eq .Role "user" }}
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<|im_start|>user
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{{ .Content }}<|im_end|>
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{{- else if eq .Role "assistant" }}
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<|im_start|>assistant
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{{ .Content }}<|im_end|>
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{{- end }}
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{{- end }}
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<|im_start|>assistant
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"""
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SYSTEM "You are a helpful assistant."
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EOF
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ollama create qwen3-tiny-v2 -f Modelfile
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ollama run qwen3-tiny-v2
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```
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### With llama.cpp
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```bash
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# Interactive chat
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llama-cli -m Qwen3-g023-tiny-v2-Q8_0.gguf \
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--chat-template chatml -cnv
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# Thinking mode
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llama-cli -m Qwen3-g023-tiny-v2-Q8_0.gguf \
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-p "<|im_start|>user\nExplain quantum computing<|im_end|>\n<|im_start|>assistant\n<think>\n" \
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-n 512
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# Non-thinking mode
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llama-cli -m Qwen3-g023-tiny-v2-Q8_0.gguf \
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-p "<|im_start|>user\n/no_think What is 2+2?<|im_end|>\n<|im_start|>assistant\n" \
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-n 128
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```
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### With Python (llama-cpp-python)
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```python
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from llama_cpp import Llama
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model = Llama("Qwen3-g023-tiny-v2-Q8_0.gguf", n_ctx=4096)
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response = model.create_chat_completion(
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "What is the capital of France?"},
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],
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temperature=0.6,
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)
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print(response["choices"][0]["message"]["content"])
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```
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## System Requirements
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| Quantization | RAM (CPU) | VRAM (GPU) |
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| Q8_0 | ~2.2 GB | ~2.2 GB |
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| Q6_K | ~1.8 GB | ~1.8 GB |
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| Q4_K_M | ~1.4 GB | ~1.4 GB |
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| Q3_K_M | ~1.2 GB | ~1.2 GB |
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| Q2_K | ~1.0 GB | ~1.0 GB |
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## v1 vs v2
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This model (v2) is the **Phase 2 champion**, using advanced multi-operation surgery for the highest overall score.
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| | [v1](https://huggingface.co/g023/Qwen3-g023-tiny-v1-GGUF) | v2 (this model) |
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|:---|:---:|:---:|
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| Layers | 27 | 30 |
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| Parameters | ~1.67B | ~1.82B |
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| Operations | del + swap | swap + interpolate + bridge |
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| Score | 92.9 / 100 | 94.3 / 100 |
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| Factual | 100% (17/17) | 94% (16/17) |
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| Perplexity | 15.70 | 15.17 |
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| Use Case | Max factual accuracy | Max overall score |
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**v1** is recommended when factual accuracy is paramount (100% vs 94%).
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**v2** is recommended when overall quality matters more (94.3 vs 92.9).
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## Methodology
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Layer surgery was performed through a systematic, test-driven process across two phases:
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1. **Phase 1** (~150 configs): Exhaustive search across deletion, duplication, swapping, interpolation, and combined operations → champion: del_10 + swap_11↔12 (v1)
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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)
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3. **Evaluation**: Each configuration scored on factual accuracy (17 questions), completion coherence, perplexity, repetition ratio, and thinking mode functionality
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### Phase 2 Leaderboard (Top 5)
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| Rank | Configuration | Score | Factual | PPL |
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|:---:|:---|:---:|:---:|:---:|
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| 🥇 | swap(12↔13,16↔17) + interp(20↔22) + bridge(5→20) | **94.3** | 94% | 15.17 |
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| 🥈 | swap(12↔13,16↔17) + interp(20↔22) | 93.9 | 94% | 14.74 |
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| 🥉 | swap(12↔13) + interp(20↔22) + bridge(5→20) | 93.4 | 94% | 15.66 |
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| 4 | multi-swap(12↔13,16↔17) | 93.1 | 100% | 14.90 |
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| 5 | Phase 1 champion (del_10 + swap_11↔12) | 92.9 | 100% | 15.70 |
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## Credits
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- **Base model**: [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) by the Qwen team at Alibaba
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- **Quantization**: llama.cpp
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- **Surgery**: g023
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## License
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Apache 2.0 — same as the original Qwen3-1.7B model.
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