What are the hardware requirements for HappyLlama1?

HappyLlama1 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 HappyLlama1 and how large is it?

HappyLlama1 is developed by ehristoforu, 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 HappyLlama1?

HappyLlama1 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

HappyLlama1 by ehristoforu — VRAM 16.1GB, 128K context

Name: HappyLlama1
Author: ehristoforu

HappyLlama1 is an open-source language model by ehristoforu. Features: 8b LLM, VRAM: 16.1GB, Context: 128K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.23.

Base model:finetune:voidful/ll... Base model:voidful/llama-3.2-8... Conversational En Endpoints compatible Instruct Llama Model-index Region:us Safetensors Sft Sharded Tensorflow Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/ehristoforu/HappyLlama1

HappyLlama1 Parameters and Internals

LLM Name	HappyLlama1
Repository 🤗	https://huggingface.co/ehristoforu/HappyLlama1
Base Model(s)	voidful/Llama-3.2-8B-Instruct voidful/Llama-3.2-8B-Instruct
Model Size	8b
Required VRAM	16.1 GB
Updated	2026-06-26
Maintainer	ehristoforu
Model Type	llama
Instruction-Based	Yes
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
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.46.3
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|finetune_right_pad_id\|>
Vocabulary Size	128256
Torch Data Type	float16

Rank the HappyLlama1 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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