What are the hardware requirements for Luck Qwen3 4B Code FineTune?

Luck Qwen3 4B Code FineTune requires approximately 8.1 GB of VRAM and supports a context window of 256K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Luck Qwen3 4B Code FineTune and how large is it?

Luck Qwen3 4B Code FineTune is developed by ahmetggg, a model with 4b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Luck Qwen3 4B Code FineTune?

Luck Qwen3 4B Code FineTune is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Luck Qwen3 4B Code FineTune by ahmetggg — VRAM 8.1GB, 256K context

Name: Luck Qwen3 4B Code FineTune
Author: ahmetggg

Luck Qwen3 4B Code FineTune is an open-source language model by ahmetggg. Features: 4b LLM, VRAM: 8.1GB, Context: 256K, License: apache-2.0, Fine-Tuned, Instruction-Based, LLM Explorer Score: 0.31.

Agentic Base model:adapter:unsloth/qwe... Base model:unsloth/qwen3-4b-in... Calibration Code Conversational Dataset:agent-ark/toucan-1.5m Dataset:nvidia/open-swe-traces Dataset:open-thoughts/openthou... En Finetuned Instruct Lora Peft Qlora Qwen3 Reasoning Region:us Safetensors Sharded Tensorflow Tool-use Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/ahmetggg/Luck-Qwen3-4b-Code-FineTune

Luck Qwen3 4B Code FineTune Parameters and Internals

LLM Name	Luck Qwen3 4B Code FineTune
Repository 🤗	https://huggingface.co/ahmetggg/Luck-Qwen3-4b-Code-FineTune
Base Model(s)	Qwen3 4B Instruct 2507 unsloth/Qwen3-4B-Instruct-2507
Model Size	4b
Required VRAM	8.1 GB
Updated	2026-09-02
Maintainer	ahmetggg
Model Type	qwen3
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-2 3.1 GB: 2-of-2
Supported Languages	en
Model Architecture	Qwen3ForCausalLM
License	apache-2.0
Context Length	262144
Model Max Length	262144
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|vision_pad\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Luck Qwen3 4B Code FineTune capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Luck Qwen3 4B Code FineTune

Best Alternatives	Context / RAM	Downloads	Likes
FastContext 1.0 4B SFT	256K / 8.1 GB	5735	357
Qwen3 4B Instruct 2507	256K / 8.1 GB	3109972	911
Fable Traces	256K / 8.1 GB	358	210
FastContext 1.0 4B RL	256K / 8.1 GB	4559	61
Qwen3 4B Instruct 2507 FP8	256K / 5.2 GB	1100744	78
Qwen3 4B Sft Prm	256K / 17.6 GB	602	0
...pt Full Qwen3 4b Instruct 2507	256K / 8 GB	365	1
Nexa AI 4B Instruct	256K / 8 GB	576	2
Neuron 4B Instruct	256K / 8.1 GB	313	1
Agents K1	256K / 8.8 GB	1098	28

Note: green Score (e.g. "73.2") means that the model is better than ahmetggg/Luck-Qwen3-4b-Code-FineTune.