---

base_model: ahmetggg/Luck-Qwen3-4b-Code-FineTune

base_model_relation: quantized

license: apache-2.0

tags:

- gguf

- qwen3

- code

- tool-use

- agentic

- unsloth

language:

- en

---

Luck-Qwen3-4b-Code-FineTune-GGUF

This repository contains official GGUF quantizations for ahmetggg/Luck-Qwen3-4b-Code-FineTune (a 4.05B parameter QLoRA fine-tune optimized for agentic coding, tool use, multi-step reasoning, and calibrated uncertainty). Complete training logs, dataset details, and base weights are in the main repository.

========================================================================

SCORES

========================================================================

category base fine-tune delta

---------------------------------------------

tool 93.8% 0.0% -93.8pp

multistep 90.0% 0.0% -90.0pp

calib_answer 100.0% 87.5% -12.5pp

calib_idk 100.0% 10.0% -90.0pp

reason 100.0% 100.0% +0.0pp

code 87.5% 0.0% -87.5pp

agent 100.0% 50.0% -50.0pp

---------------------------------------------

FOCUS avg 95.9% 15.0% -80.9pp

CALIBRATION 100.0% 48.8% -51.2pp

MACRO avg 95.9% 35.4% -60.5pp

KARAR: KALDI

- FOCUS ortalama geriledi; fine-tune bu sette fayda göstermedi.

- IDK setinde iyileşme yok; belirsizlik kalibrasyonu bu sette görünmedi.

- Tool-call skoru düşmüş; şablon kayması olabilir.

- Kod pass oranı düşmüş — tavan/genel coding gerilemesi var, iddia ile çelişebilir.

- Bu SWE-bench / BFCL değildir. Hold-out mini set: tool format, ilk-aksiyon, R-Tuning tarzı IDK, kısa muhakeme, unit-test kod, repo-ajan muhakemesi.

elapsed: 12.1 min

raw: /content/luck_eval_results/raw_results.json

========================================================================

<p align="center">

<img src="training_loss.png" alt="Training Loss" width="100%">

</p>

Prompt Template

This model uses standard ChatML format (no thinking/reasoning tags):

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
Write a Python function to check for prime numbers.<|im_end|>
<|im_start|>assistant

Provided Quantizations

All files are located in the repository root, generated via llama.cpp's convert_hf_to_gguf.py and llama-quantize.

| Quant | File Name | Size | Notes |

| ------ | ------------------------- | ------- | ------------------------------------------ |

| F16 | Luck-Qwen3-4B-F16.gguf | ~8.1 GB | Unquantized full precision |

| Q8_0 | Luck-Qwen3-4B-Q8_0.gguf | ~4.3 GB | Extremely high fidelity |

| Q6_K | Luck-Qwen3-4B-Q6_K.gguf | ~3.4 GB | Near-lossless quality |

| Q5_K_M | Luck-Qwen3-4B-Q5_K_M.gguf | ~2.9 GB | High quality, low loss |

| Q5_K_S | Luck-Qwen3-4B-Q5_K_S.gguf | ~2.7 GB | Slightly smaller Q5 variant |

| Q4_K_M | Luck-Qwen3-4B-Q4_K_M.gguf | ~2.5 GB | Recommended: Optimal speed/quality balance |

| Q4_K_S | Luck-Qwen3-4B-Q4_K_S.gguf | ~2.3 GB | Compact 4-bit quantization |

| IQ4_NL | Luck-Qwen3-4B-IQ4_NL.gguf | ~2.2 GB | Non-linear 4-bit quantization |

| IQ4_XS | Luck-Qwen3-4B-IQ4_XS.gguf | ~2.1 GB | Extra small 4-bit quantization |

| Q3_K_M | Luck-Qwen3-4B-Q3_K_M.gguf | ~1.9 GB | Medium 3-bit quantization |

| Q3_K_S | Luck-Qwen3-4B-Q3_K_S.gguf | ~1.8 GB | Small 3-bit quantization |

| IQ3_M | Luck-Qwen3-4B-IQ3_M.gguf | ~1.7 GB | Advanced 3-bit quantization |

| IQ3_XS | Luck-Qwen3-4B-IQ3_XS.gguf | ~1.5 GB | Extra small 3-bit quantization |

| Q2_K | Luck-Qwen3-4B-Q2_K.gguf | ~1.4 GB | High compression, lower accuracy |

| IQ2_M | Luck-Qwen3-4B-IQ2_M.gguf | ~1.3 GB | Extreme compression |

Usage Instructions

Use with llama.cpp

Run directly using Hugging Face integration:

./llama-cli -hf ahmetggg/Luck-Qwen3-4b-Code-FineTune-GGUF:Q4_K_M -p "Write a Python script to parse JSON."

Use with Ollama

Run directly via the Hugging Face hub integration:

ollama run hf.co/ahmetggg/Luck-Qwen3-4b-Code-FineTune-GGUF:Q4_K_M

Use with LM Studio

Search for ahmetggg/Luck-Qwen3-4b-Code-FineTune-GGUF within the LM Studio search bar and download your desired quantization directly.

Note: Standard downstream benchmarks (SWE-bench, BFCL, etc.) have not been conducted yet; results will be published once available.