What are the hardware requirements for Tiny Random LlamaForCausalLM?

Tiny Random LlamaForCausalLM requires a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Tiny Random LlamaForCausalLM and how large is it?

Tiny Random LlamaForCausalLM is developed by hmellor, a model with 1.1m 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 Tiny Random LlamaForCausalLM?

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

Tiny Random LlamaForCausalLM by hmellor — 8K context

Name: Tiny Random LlamaForCausalLM
Author: hmellor

Tiny Random LlamaForCausalLM is an open-source language model by hmellor. Features: 1.1m LLM, Context: 8K, LLM Explorer Score: 0.3.

Arxiv:1910.09700 Conversational Endpoints compatible Llama Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/hmellor/tiny-random-LlamaForCausalLM

Tiny Random LlamaForCausalLM Parameters and Internals

LLM Name	Tiny Random LlamaForCausalLM
Repository 🤗	https://huggingface.co/hmellor/tiny-random-LlamaForCausalLM
Model Size	1.1m
Required VRAM	0 GB
Updated	2026-08-13
Maintainer	hmellor
Model Type	llama
Model Files	0.0 GB
Model Architecture	LlamaForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.51.3
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Tiny Random LlamaForCausalLM 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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