What are the hardware requirements for Codellama 13B Instruct Nf4 Fp16 Upscaled?

Codellama 13B Instruct Nf4 Fp16 Upscaled requires approximately 26 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 13B Instruct Nf4 Fp16 Upscaled and how large is it?

Codellama 13B Instruct Nf4 Fp16 Upscaled is developed by arnavgrg, 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 13B Instruct Nf4 Fp16 Upscaled?

Codellama 13B Instruct Nf4 Fp16 Upscaled is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Codellama 13B Instruct Nf4 Fp16 Upscaled by arnavgrg — VRAM 26GB, 16K context

Name: Codellama 13B Instruct Nf4 Fp16 Upscaled
Author: arnavgrg

Codellama 13B Instruct Nf4 Fp16 Upscaled is an open-source language model by arnavgrg. Features: 13b LLM, VRAM: 26GB, Context: 16K, License: apache-2.0, Quantized, Instruction-Based, Code Generating, LLM Explorer Score: 0.1.

Codegen Endpoints compatible Fp16 Instruct Llama Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/arnavgrg/codellama-13b-instruct-nf4-fp16-upscaled

Codellama 13B Instruct Nf4 Fp16 Upscaled Parameters and Internals

Model Type

text generation

Additional Notes

Quantization to nf4 is not lossless, and model weights for linear layers are lossy compared to the official base model.

Training Details

Methodology:

Upscaled fp16 variant after nf4 4-bit quantization

Input Output

Accepted Modalities:

text

Performance Tips:

Upscaling helps avoid quantization/dequantization costs for each inference pass.

LLM Name	Codellama 13B Instruct Nf4 Fp16 Upscaled
Repository 🤗	https://huggingface.co/arnavgrg/codellama-13b-instruct-nf4-fp16-upscaled
Model Size	13b
Required VRAM	26 GB
Updated	2026-08-07
Maintainer	arnavgrg
Model Type	llama
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-6 5.0 GB: 2-of-6 5.0 GB: 3-of-6 4.9 GB: 4-of-6 4.9 GB: 5-of-6 1.2 GB: 6-of-6
Quantization Type	fp16
Generates Code	Yes
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	16384
Model Max Length	16384
Transformers Version	4.35.2
Tokenizer Class	CodeLlamaTokenizer
Padding Token	[PAD]
Vocabulary Size	32016
Torch Data Type	float16

Rank the Codellama 13B Instruct Nf4 Fp16 Upscaled 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 13B Instruct Nf4 Fp16 Upscaled

Best Alternatives	Context / RAM	Downloads	Likes
CodeLlama 13B Instruct Fp16	16K / 26 GB	137	28
...Llama 13B Instruct Hf 4bit MLX	16K / 7.8 GB	113	3
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
CodeLlama 13B Instruct GGUF	16K / 5.4 GB	611	2
NexusRaven 13B	16K / 26 GB	37	106
CodeLlama 13B Instruct AWQ	16K / 7.2 GB	261	9

Note: green Score (e.g. "73.2") means that the model is better than arnavgrg/codellama-13b-instruct-nf4-fp16-upscaled.