What are the hardware requirements for Fast Apply V0.2 Qwen2.5 Coder 7B Ft?

Fast Apply V0.2 Qwen2.5 Coder 7B Ft requires approximately 15.2 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 Fast Apply V0.2 Qwen2.5 Coder 7B Ft and how large is it?

Fast Apply V0.2 Qwen2.5 Coder 7B Ft is developed by quocdat25, a model with 7b 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 Fast Apply V0.2 Qwen2.5 Coder 7B Ft?

Fast Apply V0.2 Qwen2.5 Coder 7B Ft is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Fast Apply V0.2 Qwen2.5 Coder 7B Ft by quocdat25 — VRAM 15.2GB, 32K context

Name: Fast Apply V0.2 Qwen2.5 Coder 7B Ft
Author: quocdat25

Fast Apply V0.2 Qwen2.5 Coder 7B Ft is an open-source language model by quocdat25. Features: 7b LLM, VRAM: 15.2GB, Context: 32K, Quantized, Code Generating, LLM Explorer Score: 0.14.

Arxiv:1910.09700 4bit Codegen Endpoints compatible Quantized Qwen2 Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/dat-lequoc/fast-apply-v0.2-qwen2.5-Coder-7B-ft

Fast Apply V0.2 Qwen2.5 Coder 7B Ft Parameters and Internals

LLM Name	Fast Apply V0.2 Qwen2.5 Coder 7B Ft
Repository 🤗	https://huggingface.co/dat-lequoc/fast-apply-v0.2-qwen2.5-Coder-7B-ft
Model Size	7b
Required VRAM	15.2 GB
Updated	2026-08-05
Maintainer	quocdat25
Model Type	qwen2
Model Files	4.9 GB: 1-of-4 4.9 GB: 2-of-4 4.3 GB: 3-of-4 1.1 GB: 4-of-4
Quantization Type	4bit
Generates Code	Yes
Model Architecture	Qwen2ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.44.2
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|PAD_TOKEN\|>
Vocabulary Size	152064
Torch Data Type	bfloat16
Errors	replace

Rank the Fast Apply V0.2 Qwen2.5 Coder 7B Ft 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 Fast Apply V0.2 Qwen2.5 Coder 7B Ft

Best Alternatives	Context / RAM	Downloads	Likes
Kronumos Kairos V2	32K / 15.2 GB	915	2
Qwen2.5 7B Security Cot	32K / 15.2 GB	364	2
Text2sql Qwen2.5 Duckdb	32K / 15.2 GB	336	1
...query Qwen2.5 7B V5.2 Hardened	32K / 15.2 GB	118	1
Securereview 7B Mlx 4bit	32K / 4.3 GB	22	4
...2.5 Coder 7B Instruct Bnb 4bit	32K / 5.5 GB	74132	13
Qwen2.5 CoderX 7B V0.7	32K / 15.2 GB	5	2
Qwen2.5 Coder 7B Instruct 4bit	32K / 4.3 GB	13602	17
Qwen2.5 Coder 7B Bnb 4bit	32K / 5.5 GB	11641	17
Qwen2.5 CoderX 7B V0.5	32K / 15.2 GB	6	2

Note: green Score (e.g. "73.2") means that the model is better than dat-lequoc/fast-apply-v0.2-qwen2.5-Coder-7B-ft.