What are the hardware requirements for Mini Llama 70M SFT?

Mini Llama 70M SFT requires approximately 0.3 GB of VRAM and supports a context window of 0.3K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Mini Llama 70M SFT and how large is it?

Mini Llama 70M SFT is developed by rootxhacker, a model with 70m 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 Mini Llama 70M SFT?

Mini Llama 70M SFT is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mini Llama 70M SFT by rootxhacker — VRAM 0.3GB, 0.3K context

Name: Mini Llama 70M SFT
Author: rootxhacker

Mini Llama 70M SFT is an open-source language model by rootxhacker. Features: 70m LLM, VRAM: 0.3GB, Context: 0.3K, LLM Explorer Score: 0.18.

Llama Region:us Safetensors Tensorboard

Model Card on HF 🤗: https://huggingface.co/rootxhacker/mini-Llama-70M-SFT

Mini Llama 70M SFT Parameters and Internals

LLM Name	Mini Llama 70M SFT
Repository 🤗	https://huggingface.co/rootxhacker/mini-Llama-70M-SFT
Model Size	70m
Required VRAM	0.3 GB
Updated	2025-09-19
Maintainer	rootxhacker
Model Type	llama
Model Files	0.3 GB 0.0 GB
Model Architecture	LlamaForCausalLM
Context Length	256
Model Max Length	256
Transformers Version	4.51.3
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	float32

Rank the Mini Llama 70M SFT 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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