What are the hardware requirements for Qwen2.5 Coder 0.5B Instruct MIFT Ja Manywords 4000?

Qwen2.5 Coder 0.5B Instruct MIFT Ja Manywords 4000 requires approximately 2 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen2.5 Coder 0.5B Instruct MIFT Ja Manywords 4000 and how large is it?

Qwen2.5 Coder 0.5B Instruct MIFT Ja Manywords 4000 is developed by Hachipo, a model with 0.5b 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 Qwen2.5 Coder 0.5B Instruct MIFT Ja Manywords 4000?

Qwen2.5 Coder 0.5B Instruct MIFT Ja Manywords 4000 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 Coder 0.5B Instruct MIFT Ja Manywords 4000 by Hachipo — VRAM 2GB, 32K context

Name: Qwen2.5 Coder 0.5B Instruct MIFT Ja Manywords 4000
Author: Hachipo

Qwen2.5 Coder 0.5B Instruct MIFT Ja Manywords 4000 is an open-source language model by Hachipo. Features: 0.5b LLM, VRAM: 2GB, Context: 32K, Instruction-Based, Code Generating, LLM Explorer Score: 0.16.

Arxiv:1910.09700 Codegen Conversational Endpoints compatible Instruct Qwen2 Region:us Safetensors Sft Trl

Model Card on HF 🤗: https://huggingface.co/Hachipo/Qwen2.5-Coder-0.5B-Instruct_MIFT-ja_manywords_4000

Qwen2.5 Coder 0.5B Instruct MIFT Ja Manywords 4000 Parameters and Internals

LLM Name	Qwen2.5 Coder 0.5B Instruct MIFT Ja Manywords 4000
Repository 🤗	https://huggingface.co/Hachipo/Qwen2.5-Coder-0.5B-Instruct_MIFT-ja_manywords_4000
Model Size	0.5b
Required VRAM	2 GB
Updated	2026-08-02
Maintainer	Hachipo
Model Type	qwen2
Instruction-Based	Yes
Model Files	2.0 GB
Generates Code	Yes
Model Architecture	Qwen2ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.47.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|im_end\|>
Vocabulary Size	151936
Torch Data Type	float32
Errors	replace

Rank the Qwen2.5 Coder 0.5B Instruct MIFT Ja Manywords 4000 capabilities

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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

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Qwen2.5 Coder 0.5B Instruct	32K / 1 GB	2321	5
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Note: green Score (e.g. "73.2") means that the model is better than Hachipo/Qwen2.5-Coder-0.5B-Instruct_MIFT-ja_manywords_4000.