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+Qwen3-g023-tiny-v1-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen3-g023-tiny-v1-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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README.md

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----
-license: apache-2.0
----
+---
+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 | [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-Q4_K_M.gguf
+
+PARAMETER temperature 0.6
+PARAMETER top_p 0.95
+PARAMETER top_k 20
+PARAMETER min_p 0.0
+
+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-Q4_K_M.gguf \
+  --chat-template chatml -cnv
+
+# Thinking mode
+llama-cli -m Qwen3-g023-tiny-v1-Q4_K_M.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-Q4_K_M.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-Q4_K_M.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.