What are the hardware requirements for Codellama CodeLlama 13B Instruct Hf W4 G128 AWQ?

Codellama CodeLlama 13B Instruct Hf W4 G128 AWQ requires approximately 7.2 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 13B Instruct Hf W4 G128 AWQ and how large is it?

Codellama CodeLlama 13B Instruct Hf W4 G128 AWQ is developed by abhinavkulkarni, a model with 13b 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 13B Instruct Hf W4 G128 AWQ?

Codellama CodeLlama 13B 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 13B Instruct Hf W4 G128 AWQ by abhinavkulkarni — VRAM 7.2GB, 16K context

Name: Codellama CodeLlama 13B Instruct Hf W4 G128 AWQ
Author: abhinavkulkarni

Codellama CodeLlama 13B Instruct Hf W4 G128 AWQ is an open-source language model by abhinavkulkarni. Features: 13b LLM, VRAM: 7.2GB, 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 Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/abhinavkulkarni/codellama-CodeLlama-13b-Instruct-hf-w4-g128-awq

Codellama CodeLlama 13B Instruct Hf W4 G128 AWQ Parameters and Internals

Model Type

generative, text generation

Use Cases

Areas:

research, commercial applications

Additional Notes

The model uses AWQ quantization for efficiency and supports CUDA and Docker configurations.

Input Output

Input Format:

text prompt

Accepted Modalities:

text

Output Format:

text generation

LLM Name	Codellama CodeLlama 13B Instruct Hf W4 G128 AWQ
Repository 🤗	https://huggingface.co/abhinavkulkarni/codellama-CodeLlama-13b-Instruct-hf-w4-g128-awq
Model Size	13b
Required VRAM	7.2 GB
Updated	2026-05-30
Maintainer	abhinavkulkarni
Model Type	llama
Instruction-Based	Yes
Model Files	3.2 GB: 1-of-3 3.2 GB: 2-of-3 0.8 GB: 3-of-3 3.2 GB: 1-of-3 3.2 GB: 2-of-3 0.8 GB: 3-of-3
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 13B 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

Best Alternatives to Codellama CodeLlama 13B Instruct Hf W4 G128 AWQ

Best Alternatives	Context / RAM	Downloads	Likes
CodeLlama 13B Instruct AWQ	16K / 7.2 GB	261	9
NexusRaven 13B AWQ	16K / 7.2 GB	22	4
CodeLlama 13B Instruct Fp16	16K / 26 GB	137	28
...Llama 13B Instruct Hf 4bit MLX	16K / 7.8 GB	113	3
...13B Instruct Nf4 Fp16 Upscaled	16K / 26 GB	6	0
NexusRaven V2 13B	16K / 26 GB	105	472
CodeLlama 13B Instruct Hf	16K / 26 GB	2469	160
CodeLlama 13B MORepair	16K / 26 GB	9	2
CodeLlama 13B Instruct Hf	16K / 26 GB	369	29
TableLLM 13B	16K / 26 GB	98	34

Note: green Score (e.g. "73.2") means that the model is better than abhinavkulkarni/codellama-CodeLlama-13b-Instruct-hf-w4-g128-awq.