What are the hardware requirements for Qwen2.5 7B MFANN Slerp?

Qwen2.5 7B MFANN Slerp 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 Qwen2.5 7B MFANN Slerp and how large is it?

Qwen2.5 7B MFANN Slerp is developed by netcat420, 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 Qwen2.5 7B MFANN Slerp?

Qwen2.5 7B MFANN Slerp is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 7B MFANN Slerp by netcat420 — VRAM 15.2GB, 32K context

Name: Qwen2.5 7B MFANN Slerp
Author: netcat420

Qwen2.5 7B MFANN Slerp is an open-source language model by netcat420. Features: 7b LLM, VRAM: 15.2GB, Context: 32K, Instruction-Based, Code Generating, Merged, LLM Explorer Score: 0.24.

Merged Model Autotrain compatible Base model:huihui-ai/qwen2.5-c... Base model:netcat420/qwen2.5-m... Codegen Conversational Endpoints compatible Instruct Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/netcat420/Qwen2.5-7b-MFANN-slerp

Qwen2.5 7B MFANN Slerp Parameters and Internals

LLM Name	Qwen2.5 7B MFANN Slerp
Repository 🤗	https://huggingface.co/netcat420/Qwen2.5-7b-MFANN-slerp
Base Model(s)	huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated netcat420/Qwen2.5-MFANN-7b huihui-ai/Qwen2.5-Coder-7B-Instruct-abliterated netcat420/Qwen2.5-MFANN-7b
Merged Model	Yes
Model Size	7b
Required VRAM	15.2 GB
Updated	2025-02-02
Maintainer	netcat420
Model Type	qwen2
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-4 4.9 GB: 2-of-4 5.0 GB: 3-of-4 0.3 GB: 4-of-4
Generates Code	Yes
Model Architecture	Qwen2ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.46.2
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|im_end\|>
Vocabulary Size	152064
Torch Data Type	float16
Errors	replace

Rank the Qwen2.5 7B MFANN Slerp 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 7B MFANN Slerp

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 7B Rebase	986K / 15.2 GB	73	2
Qwen2.5 7B Rebase	986K / 15.2 GB	16	2
StockQwen 2.5 7B	128K / 15.2 GB	69	6
DareQwen 2.5 7B	128K / 15.2 GB	11	1
Tessa T1 7B	117K / 15.2 GB	14	10
UIGEN T1.5 7B	117K / 15.2 GB	12	6
Qwen2.5 Coder 7B Instruct	32K / 15.2 GB	2019400	765
Vibe Coder 7B Max	32K / 15.3 GB	460	1
Finsec Detector	32K / 15.2 GB	390	1
Qwen2.5 Coder Malay 7B	32K / 15.2 GB	195	1

Note: green Score (e.g. "73.2") means that the model is better than netcat420/Qwen2.5-7b-MFANN-slerp.