What are the hardware requirements for Magicoder S CL 7B 4.0bpw H6 EXL2 2?

Magicoder S CL 7B 4.0bpw H6 EXL2 2 requires approximately 3.6 GB of VRAM and supports a context window of 16K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Magicoder S CL 7B 4.0bpw H6 EXL2 2 and how large is it?

Magicoder S CL 7B 4.0bpw H6 EXL2 2 is developed by LoneStriker, 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 Magicoder S CL 7B 4.0bpw H6 EXL2 2?

Magicoder S CL 7B 4.0bpw H6 EXL2 2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Magicoder S CL 7B 4.0bpw H6 EXL2 2 by LoneStriker — VRAM 3.6GB, 16K context

Name: Magicoder S CL 7B 4.0bpw H6 EXL2 2
Author: LoneStriker

Magicoder S CL 7B 4.0bpw H6 EXL2 2 is an open-source language model by LoneStriker. Features: 7b LLM, VRAM: 3.6GB, Context: 16K, License: llama2, Quantized, Instruction-Based, LLM Explorer Score: 0.1.

Arxiv:2305.06161 Arxiv:2312.02120 Dataset:ise-uiuc/magicoder-evo... Dataset:ise-uiuc/magicoder-oss... Endpoints compatible Exl2 Instruct Llama Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/LoneStriker/Magicoder-S-CL-7B-4.0bpw-h6-exl2-2

Magicoder S CL 7B 4.0bpw H6 EXL2 2 Parameters and Internals

Model Type

text-generation

Use Cases

Areas:

coding tasks

Limitations:

Magicoders may not work well in non-coding tasks, May produce misleading contents or struggle with non-coding instructions

Considerations:

Users should be aware of the risks, biases, and limitations of the model.

Training Details

Data Sources:

ise-uiuc/Magicoder-OSS-Instruct-75K, ise-uiuc/Magicoder-Evol-Instruct-110K

Methodology:

OSS-Instruct approach using open-source repositories for generating instruction data

Input Output

Input Format:

Prompt-based input within MAGICODER_PROMPT template

Accepted Modalities:

text

Output Format:

Text

Performance Tips:

Ensure the transformer library is installed

LLM Name	Magicoder S CL 7B 4.0bpw H6 EXL2 2
Repository 🤗	https://huggingface.co/LoneStriker/Magicoder-S-CL-7B-4.0bpw-h6-exl2-2
Model Size	7b
Required VRAM	3.6 GB
Updated	2026-05-22
Maintainer	LoneStriker
Model Type	llama
Instruction-Based	Yes
Model Files	3.6 GB
Quantization Type	exl2
Model Architecture	LlamaForCausalLM
License	llama2
Context Length	16384
Model Max Length	16384
Transformers Version	4.36.0.dev0
Tokenizer Class	CodeLlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float32

Rank the Magicoder S CL 7B 4.0bpw H6 EXL2 2 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 Magicoder S CL 7B 4.0bpw H6 EXL2 2

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...p 0.05 Max Grad1.0 Grad Accu32	32K / 14.4 GB	9	0
...p 0.05 Max Grad1.0 Grad Accu32	32K / 14.4 GB	6	0
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...B Instruct Hf Bnb 4bit Smashed	16K / 4.2 GB	6	0
CodelLama7B Inst DPO 7K Mlx	16K / 4.2 GB	49	4
...eLlama 7B Instruct Hf 4bit MLX	16K / 4.2 GB	84	3
...6.7B Instruct 8.0bpw H8 EXL2 2	16K / 6.8 GB	7	2
...6.7B Instruct 3.0bpw H6 EXL2 2	16K / 2.8 GB	8	1
...coder S CL 7B 5.0bpw H6 EXL2 2	16K / 4.4 GB	6	1
...coder S CL 7B 3.0bpw H6 EXL2 2	16K / 2.8 GB	2	1

Note: green Score (e.g. "73.2") means that the model is better than LoneStriker/Magicoder-S-CL-7B-4.0bpw-h6-exl2-2.