What are the hardware requirements for Qwen2.5 Slick Coder 14B Instruct?

Qwen2.5 Slick Coder 14B Instruct requires approximately 29.7 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 Slick Coder 14B Instruct and how large is it?

Qwen2.5 Slick Coder 14B Instruct is developed by spacematt, a model with 14b 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 Slick Coder 14B Instruct?

Qwen2.5 Slick Coder 14B Instruct is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 Slick Coder 14B Instruct by spacematt — VRAM 29.7GB, 32K context

Name: Qwen2.5 Slick Coder 14B Instruct
Author: spacematt

Qwen2.5 Slick Coder 14B Instruct is an open-source language model by spacematt. Features: 14b LLM, VRAM: 29.7GB, Context: 32K, License: apache-2.0, Instruction-Based, Code Generating, Merged, LLM Explorer Score: 0.18.

Merged Model Arxiv:2403.19522 Autotrain compatible Base model:allknowingroger/qwe... Base model:hotmailuser/virtuos... Base model:nbeerbower/eva-abli... Base model:prithivmlmods/messi... Base model:qihoo360/light-r1-1... Base model:qingy2024/fusion4-1... Base model:sakalti/saka-14b Base model:spacematt/qwen2.5-s... Base model:suayptalha/lix-14b-... Base model:tesslate/tessa-t1-1... Base model:v000000/qwen2.5-lum... Codegen Conversational Endpoints compatible Instruct Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/spacematt/Qwen2.5-Slick-Coder-14B-Instruct

Qwen2.5 Slick Coder 14B Instruct Parameters and Internals

LLM Name	Qwen2.5 Slick Coder 14B Instruct
Repository 🤗	https://huggingface.co/spacematt/Qwen2.5-Slick-Coder-14B-Instruct
Base Model(s)	mergekit-community/Qwen2.5-14B-Merge QwenSlerp5 14B Fusion4 14B Instruct Messier Opus 14B Elite7 Saka 14B Light R1 14B DS Virtuoso Slerp 14B nbeerbower/EVA-abliterated-TIES-Qwen2.5-14B suayptalha/Lix-14B-v0.1 v000000/Qwen2.5-Lumen-14B spacematt/Qwen2.5-Smooth-Coder-14B-Instruct Tessa T1 14B mergekit-community/Qwen2.5-14B-Merge allknowingroger/QwenSlerp5-14B qingy2024/Fusion4-14B-Instruct prithivMLmods/Messier-Opus-14B-Elite7 Sakalti/Saka-14B qihoo360/Light-R1-14B-DS hotmailuser/Virtuoso-Slerp-14B nbeerbower/EVA-abliterated-TIES-Qwen2.5-14B suayptalha/Lix-14B-v0.1 v000000/Qwen2.5-Lumen-14B spacematt/Qwen2.5-Smooth-Coder-14B-Instruct Tesslate/Tessa-T1-14B
Merged Model	Yes
Model Size	14b
Required VRAM	29.7 GB
Updated	2025-08-06
Maintainer	spacematt
Model Type	qwen2
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-6 5.0 GB: 2-of-6 5.0 GB: 3-of-6 5.0 GB: 4-of-6 5.0 GB: 5-of-6 4.8 GB: 6-of-6
Generates Code	Yes
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.51.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151665
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen2.5 Slick Coder 14B Instruct 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 Qwen2.5 Slick Coder 14B Instruct

Best Alternatives	Context / RAM	Downloads	Likes
ZYH LLM Qwen2.5 14B V4	986K / 29.7 GB	27	8
Qwen2.5 14B YOYO V4	986K / 29.7 GB	20	5
ZYH LLM Qwen2.5 14B V2	986K / 29.7 GB	14	1
ZYH LLM Qwen2.5 14B V5	986K / 29.7 GB	12	8
Qwen2.5 14B YOYO V5	986K / 29.7 GB	10	4
Mergekit Model Stock Ggfjmyf	986K / 29.7 GB	14	0
Mergekit Model Stock Injkqri	986K / 29.7 GB	5	1
...1 DeepSeekR1 Coder 14B Preview	128K / 29.7 GB	9	3
Tessa T1 14B	117K / 29.7 GB	6	13
UIGEN T1.5 14B	117K / 29.7 GB	8	6

Note: green Score (e.g. "73.2") means that the model is better than spacematt/Qwen2.5-Slick-Coder-14B-Instruct.