What are the hardware requirements for Llama 3 8B Tagalog V1?

Llama 3 8B Tagalog V1 requires approximately 16.1 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 3 8B Tagalog V1 and how large is it?

Llama 3 8B Tagalog V1 is developed by 922-Narra, 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 8B Tagalog V1?

Llama 3 8B Tagalog V1 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 3 8B Tagalog V1 by 922-Narra — VRAM 16.1GB, 8K context

Name: Llama 3 8B Tagalog V1
Author: 922-Narra

Llama 3 8B Tagalog V1 is an open-source language model by 922-Narra. Features: 8b LLM, VRAM: 16.1GB, Context: 8K, License: llama3, Quantized, LLM Explorer Score: 0.12.

4bit Base model:finetune:unsloth/ll... Base model:unsloth/llama-3-8b-... Dataset:922-narra/tagaloguanac... En Endpoints compatible Gguf Llama Pytorch Quantized Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/922-Narra/Llama-3-8b-tagalog-v1

Llama 3 8B Tagalog V1 Parameters and Internals

Model Type

text-generation-inference

Use Cases

Primary Use Cases:

chat

Limitations:

may switch back to English or output clunky results

Considerations:

not guaranteed to output aligned or safe outputs; not meant for production use.

Additional Notes

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. Use at your own risk.

Training Details

Data Sources:

https://huggingface.co/datasets/922-Narra/tagaloguanaco_cleaned_03152024

Methodology:

fine-tuning

Input Output

Input Format:

"\nHuman: INPUT\nAssistant:"

LLM Name	Llama 3 8B Tagalog V1
Repository 🤗	https://huggingface.co/922-Narra/Llama-3-8b-tagalog-v1
Base Model(s)	Llama 3 8B Bnb 4bit unsloth/llama-3-8b-bnb-4bit
Model Size	8b
Required VRAM	16.1 GB
Updated	2026-09-11
Maintainer	922-Narra
Model Type	llama
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 4.9 GB: 3-of-4 1.2 GB: 4-of-4 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	en
GGUF Quantization	Yes
Quantization Type	gguf\|4bit
Model Architecture	LlamaForCausalLM
License	llama3
Context Length	8192
Model Max Length	8192
Transformers Version	4.40.2
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|reserved_special_token_250\|>
Vocabulary Size	128256
Torch Data Type	float16

Rank the Llama 3 8B Tagalog V1 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 Llama 3 8B Tagalog V1

Best Alternatives	Context / RAM	Downloads	Likes
10K V6	1024K / 16.1 GB	5	0
...truct Gradient 1048K IMat GGUF	1024K / 2 GB	650	6
...B Instruct Gradient 1048K GGUF	1024K / 3.2 GB	445	3
Unhinged Llama3 8B 524K	512K / 26.5 GB	25	0
Llama 3 8B Instruct 262K GGUF	256K / 3.2 GB	208	2
Dolphin3 Cyber 8B GGUF	128K / 3.2 GB	59950	150
Grok Oss Apollyon 8B	128K / 16.1 GB	3449	8
Dolphin3 Cyber 8B GGUF	128K / 3.2 GB	456	1
...3 8B Instruct 128K Jbliterated	128K / 4.9 GB	384	3
Delentia Slm Jitna V0.4	128K / 16.1 GB	310	1

Note: green Score (e.g. "73.2") means that the model is better than 922-Narra/Llama-3-8b-tagalog-v1.