What are the hardware requirements for Deepseek Coder 7B Instruct V1.5 8.0bpw H8 EXL2?

Deepseek Coder 7B Instruct V1.5 8.0bpw H8 EXL2 requires approximately 7.3 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Deepseek Coder 7B Instruct V1.5 8.0bpw H8 EXL2 and how large is it?

Deepseek Coder 7B Instruct V1.5 8.0bpw H8 EXL2 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 Deepseek Coder 7B Instruct V1.5 8.0bpw H8 EXL2?

Deepseek Coder 7B Instruct V1.5 8.0bpw H8 EXL2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Deepseek Coder 7B Instruct V1.5 8.0bpw H8 EXL2 by LoneStriker — VRAM 7.3GB, 4K context

Name: Deepseek Coder 7B Instruct V1.5 8.0bpw H8 EXL2
Author: LoneStriker

Deepseek Coder 7B Instruct V1.5 8.0bpw H8 EXL2 is an open-source language model by LoneStriker. Features: 7b LLM, VRAM: 7.3GB, Context: 4K, License: other, Quantized, Instruction-Based, Code Generating, LLM Explorer Score: 0.1.

Codegen Conversational Endpoints compatible Exl2 Instruct Llama Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/LoneStriker/deepseek-coder-7b-instruct-v1.5-8.0bpw-h8-exl2

Deepseek Coder 7B Instruct V1.5 8.0bpw H8 EXL2 Parameters and Internals

Model Type

text generation, inference

Training Details

Data Volume:

2T tokens for pre-training and 2B tokens for fine-tuning

Methodology:

Pre-training with a window size of 4K and next token prediction; fine-tuning on instruction data

Context Length:

4096

LLM Name	Deepseek Coder 7B Instruct V1.5 8.0bpw H8 EXL2
Repository 🤗	https://huggingface.co/LoneStriker/deepseek-coder-7b-instruct-v1.5-8.0bpw-h8-exl2
Model Size	7b
Required VRAM	7.3 GB
Updated	2026-08-03
Maintainer	LoneStriker
Model Type	llama
Instruction-Based	Yes
Model Files	7.3 GB
Quantization Type	exl2
Generates Code	Yes
Model Architecture	LlamaForCausalLM
License	other
Context Length	4096
Model Max Length	4096
Transformers Version	4.35.2
Tokenizer Class	LlamaTokenizerFast
Beginning of Sentence Token	<｜begin▁of▁sentence｜>
End of Sentence Token	<\|EOT\|>
Vocabulary Size	102400
Torch Data Type	bfloat16

Rank the Deepseek Coder 7B Instruct V1.5 8.0bpw H8 EXL2 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 Deepseek Coder 7B Instruct V1.5 8.0bpw H8 EXL2

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...ruct Solidity Bnb 4bit Smashed	16K / 4.2 GB	6	0
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...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
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... 7B Instruct Nf4 Fp16 Upscaled	16K / 13.5 GB	9	0
CodeLlama 7B Instruct Fp16	16K / 13.5 GB	24	7
...Llama 7B Instruct Bf16 Sharded	16K / 13.5 GB	29	1
...B Instruct V1.5 6.0bpw H6 EXL2	4K / 5.7 GB	8	2

Note: green Score (e.g. "73.2") means that the model is better than LoneStriker/deepseek-coder-7b-instruct-v1.5-8.0bpw-h8-exl2.