What are the hardware requirements for Pharma Tinyllama Instruction Merged?

Pharma Tinyllama Instruction Merged requires approximately 2.2 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 Pharma Tinyllama Instruction Merged and how large is it?

Pharma Tinyllama Instruction Merged is developed by SivaSai8143, a model with 1.1b 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 Pharma Tinyllama Instruction Merged?

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

Pharma Tinyllama Instruction Merged by SivaSai8143 — VRAM 2.2GB, 2K context

Name: Pharma Tinyllama Instruction Merged
Author: SivaSai8143

Pharma Tinyllama Instruction Merged is an open-source language model by SivaSai8143. Features: 1.1b LLM, VRAM: 2.2GB, Context: 2K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.25.

Base model:adapter:sivasai8143... Base model:sivasai8143/pharma-... Domain-adaptation En Instruct Llama Lora Peft Pharma Qlora Region:us Safetensors Sft Tinyllama

Model Card on HF 🤗: https://huggingface.co/SivaSai8143/pharma-tinyllama-instruction-merged

Pharma Tinyllama Instruction Merged Parameters and Internals

LLM Name	Pharma Tinyllama Instruction Merged
Repository 🤗	https://huggingface.co/SivaSai8143/pharma-tinyllama-instruction-merged
Base Model(s)	SivaSai8143/pharma-tinyllama-non-instruction-merged SivaSai8143/pharma-tinyllama-non-instruction-merged
Model Size	1.1b
Required VRAM	2.2 GB
Updated	2026-07-30
Maintainer	SivaSai8143
Model Type	llama
Instruction-Based	Yes
Model Files	2.2 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	5.12.1
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000

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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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