What are the hardware requirements for Tinyllama Quant?

Tinyllama Quant requires approximately 0.8 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Tinyllama Quant and how large is it?

Tinyllama Quant is developed by ElxsiGenwizards. 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 Tinyllama Quant?

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

Tinyllama Quant by ElxsiGenwizards — VRAM 0.8GB, 2K context

Name: Tinyllama Quant
Author: ElxsiGenwizards

Tinyllama Quant is an open-source language model by ElxsiGenwizards. Features: LLM, VRAM: 0.8GB, Context: 2K, License: mit, LLM Explorer Score: 0.12.

Arxiv:2309.05463 Code En Endpoints compatible Phi Region:us

Model Card on HF 🤗: https://huggingface.co/ElxsiGenwizards/tinyllama-quant

Tinyllama Quant Parameters and Internals

Model Type

text generation

Use Cases

Primary Use Cases:

writing poems, drafting emails, creating stories, summarizing texts, writing Python code

Limitations:

generate inaccurate code and facts, limited scope for code, unreliable responses to instruction, language limitations, potential societal biases, toxicity

Additional Notes

Phi-1.5 is best suited for prompts using the QA format, the chat format, and the code format. It may produce irrelevant text following the main answer.

Supported Languages

en (standard)

Training Details

Data Sources:

same data sources as phi-1, augmented with a new data source that consists of various NLP synthetic texts

Data Volume:

150B tokens

Training Time:

8 days

Hardware Used:

32xA100-40G

Model Architecture:

Transformer-based model with next-word prediction objective

LLM Name	Tinyllama Quant
Repository 🤗	https://huggingface.co/ElxsiGenwizards/tinyllama-quant
Required VRAM	0.8 GB
Updated	2026-07-03
Maintainer	ElxsiGenwizards
Model Type	phi
Model Files	0.0 GB 0.8 GB 0.8 GB
Supported Languages	en
Model Architecture	PhiForCausalLM
License	mit
Context Length	2048
Model Max Length	2048
Transformers Version	4.37.0
Tokenizer Class	CodeGenTokenizer
Vocabulary Size	51200
Torch Data Type	float16

Rank the Tinyllama Quant 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 Tinyllama Quant

Best Alternatives	Context / RAM	Downloads
Merlin1.4	2K / 5.6 GB	74
Merlin1.5	2K / 5.6 GB	99
Merlin1.2	2K / 5.6 GB	65
Merlin1.3	2K / 5.6 GB	67
Phi1.5 Quantized 2	2K / GB	5
Phi1.5 Update 4	2K / GB	6
Phi 2 Fp8	2K / 3.3 GB	89
Phi Medquad Frequency	2K / 5.6 GB	5
Phi 2 Fp8	2K / 3.3 GB	7
Phi 2 Int8 Ov	2K / 0 GB	22

Note: green Score (e.g. "73.2") means that the model is better than ElxsiGenwizards/tinyllama-quant.