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Luck-Qwen3-4b-Code-FineTune-GGUF
ahmetggg/Luck-Qwen3-4b-Code-FineTune-GGUF
Luck-Qwen3-4b-Code-FineTune-GGUF is a machine learning model from ahmetggg. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
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…
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Aug 31, 2026
- Updated
- Sep 1, 2026
- SHA
- db46dde4
Base models
Model card
From the Hugging Face model README
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): ```text <|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.
- License
- apache-2.0
- Languages
- en
- Created
- Aug 31, 2026
- Updated
- Sep 1, 2026