What are the hardware requirements for Codellama CodeLlama 7B Instruct Hf W4 G128 AWQ?

Codellama CodeLlama 7B Instruct Hf W4 G128 AWQ requires approximately 3.9 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 Codellama CodeLlama 7B Instruct Hf W4 G128 AWQ and how large is it?

Codellama CodeLlama 7B Instruct Hf W4 G128 AWQ is developed by abhinavkulkarni, 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 Codellama CodeLlama 7B Instruct Hf W4 G128 AWQ?

Codellama CodeLlama 7B Instruct Hf W4 G128 AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Codellama CodeLlama 7B Instruct Hf W4 G128 AWQ by abhinavkulkarni — VRAM 3.9GB, 16K context

Name: Codellama CodeLlama 7B Instruct Hf W4 G128 AWQ
Author: abhinavkulkarni

Codellama CodeLlama 7B Instruct Hf W4 G128 AWQ is an open-source language model by abhinavkulkarni. Features: 7b LLM, VRAM: 3.9GB, Context: 16K, License: llama2, Quantized, Instruction-Based, Code Generating, LLM Explorer Score: 0.08.

Arxiv:2308.12950 Awq Code Codegen Instruct Llama Llama2 Pytorch Quantized Region:us

Model Card on HF 🤗: https://huggingface.co/abhinavkulkarni/codellama-CodeLlama-7b-Instruct-hf-w4-g128-awq

Codellama CodeLlama 7B Instruct Hf W4 G128 AWQ Parameters and Internals

Model Type

generative, text

Additional Notes

The model is a collection of pretrained and fine-tuned generative text models with AWQ quantization. It is a 4-bit 128 group size AWQ quantized model.

LLM Name	Codellama CodeLlama 7B Instruct Hf W4 G128 AWQ
Repository 🤗	https://huggingface.co/abhinavkulkarni/codellama-CodeLlama-7b-Instruct-hf-w4-g128-awq
Model Size	7b
Required VRAM	3.9 GB
Updated	2026-08-06
Maintainer	abhinavkulkarni
Model Type	llama
Instruction-Based	Yes
Model Files	3.9 GB
Supported Languages	code
AWQ Quantization	Yes
Quantization Type	awq
Generates Code	Yes
Model Architecture	LlamaForCausalLM
License	llama2
Context Length	16384
Model Max Length	16384
Transformers Version	4.33.1
Tokenizer Class	CodeLlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
Vocabulary Size	32016
Torch Data Type	float16

Rank the Codellama CodeLlama 7B Instruct Hf W4 G128 AWQ 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

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CodeLlama 7B Instruct AWQ	16K / 3.9 GB	278	4
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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
... 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

Note: green Score (e.g. "73.2") means that the model is better than abhinavkulkarni/codellama-CodeLlama-7b-Instruct-hf-w4-g128-awq.