What are the hardware requirements for Llama 3.2 1B AllDataSources 5e 05 Constant 512 Flattening?

Llama 3.2 1B AllDataSources 5e 05 Constant 512 Flattening requires approximately 2.5 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.

Where can I download or evaluate Llama 3.2 1B AllDataSources 5e 05 Constant 512 Flattening?

Llama 3.2 1B AllDataSources 5e 05 Constant 512 Flattening is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 3.2 1B AllDataSources 5e 05 Constant 512 Flattening by Mattia2700 — VRAM 2.5GB, 128K context

Name: Llama 3.2 1B AllDataSources 5e 05 Constant 512 Flattening
Author: Mattia2700

Llama 3.2 1B AllDataSources 5e 05 Constant 512 Flattening is an open-source language model by Mattia2700. Features: 1b LLM, VRAM: 2.5GB, Context: 128K, LLM Explorer Score: 0.17.

Arxiv:1910.09700 Endpoints compatible Llama Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/Mattia2700/Llama-3.2-1B_AllDataSources_5e-05_constant_512_flattening

Llama 3.2 1B AllDataSources 5e 05 Constant 512 Flattening Parameters and Internals

LLM Name	Llama 3.2 1B AllDataSources 5e 05 Constant 512 Flattening
Repository 🤗	https://huggingface.co/Mattia2700/Llama-3.2-1B_AllDataSources_5e-05_constant_512_flattening
Model Size	1b
Required VRAM	2.5 GB
Updated	2026-07-21
Maintainer	Mattia2700
Model Type	llama
Model Files	2.5 GB 0.0 GB
Model Architecture	LlamaForCausalLM
Context Length	131072
Model Max Length	131072
Transformers Version	4.45.0
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<pad>
Vocabulary Size	128257
Torch Data Type	bfloat16

Rank the Llama 3.2 1B AllDataSources 5e 05 Constant 512 Flattening capabilities

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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 Mattia2700/Llama-3.2-1B_AllDataSources_5e-05_constant_512_flattening.