What are the hardware requirements for Llama 3.1 8B Instuct Uz?

Llama 3.1 8B Instuct Uz requires approximately 16.1 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 3.1 8B Instuct Uz and how large is it?

Llama 3.1 8B Instuct Uz is developed by behbudiy, a model with 8b 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 Llama 3.1 8B Instuct Uz?

Llama 3.1 8B Instuct Uz is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 3.1 8B Instuct Uz by behbudiy — VRAM 16.1GB, 128K context

Name: Llama 3.1 8B Instuct Uz
Author: behbudiy

Llama 3.1 8B Instuct Uz is an open-source language model by behbudiy. Features: 8b LLM, VRAM: 16.1GB, Context: 128K, License: llama3.1, Instruction-Based, LLM Explorer Score: 0.15.

Autotrain compatible Conversational Dataset:behbudiy/alpaca-cleane... Dataset:behbudiy/translation-i... Dataset:yahma/alpaca-cleaned En Endpoints compatible Instruct Llama Lora Region:us Safetensors Sharded Summarization Tensorflow Translation Uz

Model Card on HF 🤗: https://huggingface.co/behbudiy/Llama-3.1-8B-Instuct-Uz

Llama 3.1 8B Instuct Uz Parameters and Internals

Model Type

text generation, summarization, translation, question answering

Use Cases

Areas:

research, commercial applications

Applications:

machine translation, summarization, dialogue systems

Primary Use Cases:

Uzbek Sentiment Analysis, Uzbek News Classification, MMLU

Additional Notes

This model has been optimized for Uzbek language processing tasks and can be used with `transformers` library.

Supported Languages

uz (Advanced), en (Advanced)

Training Details

Data Sources:

yahma/alpaca-cleaned, behbudiy/alpaca-cleaned-uz, behbudiy/translation-instruction

Methodology:

Instruction-tuning using a mix of publicly available and syntheticly constructed Uzbek and English data.

Input Output

Input Format:

JSON

Accepted Modalities:

text

Output Format:

text

Performance Tips:

Using the transformers 'pipeline' for conversational inference or shift to the `generate()` function for more advanced usage.

LLM Name	Llama 3.1 8B Instuct Uz
Repository 🤗	https://huggingface.co/behbudiy/Llama-3.1-8B-Instuct-Uz
Base Model(s)	models/Meta-Llama-3.1-8B-Instruct models/Meta-Llama-3.1-8B-Instruct
Model Size	8b
Required VRAM	16.1 GB
Updated	2025-08-19
Maintainer	behbudiy
Model Type	llama
Instruction-Based	Yes
Model Files	0.7 GB 5.0 GB: 1-of-4 5.0 GB: 2-of-4 4.9 GB: 3-of-4 1.2 GB: 4-of-4
Supported Languages	uz en
Model Architecture	AutoModelForCausalLM
License	llama3.1
Context Length	131072
Model Max Length	131072
Transformers Version	4.43.3
Is Biased	none
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|reserved_special_token_247\|>
Vocabulary Size	128256
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	down_proj\|up_proj\|k_proj\|v_proj\|q_proj\|gate_proj\|o_proj
LoRA Alpha	32
LoRA Dropout	0
R Param	64
Torch Data Type	bfloat16

Rank the Llama 3.1 8B Instuct Uz 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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Note: green Score (e.g. "73.2") means that the model is better than behbudiy/Llama-3.1-8B-Instuct-Uz.