What are the hardware requirements for Param 1 2.9B Instruct?

Param 1 2.9B Instruct requires approximately 5.8 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Param 1 2.9B Instruct and how large is it?

Param 1 2.9B Instruct is developed by bharatgenai, a model with 2.9b 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 Param 1 2.9B Instruct?

Param 1 2.9B Instruct is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Param 1 2.9B Instruct by bharatgenai — VRAM 5.8GB, 8K context

Name: Param 1 2.9B Instruct
Author: bharatgenai

Param 1 2.9B Instruct is an open-source language model by bharatgenai. Features: 2.9b LLM, VRAM: 5.8GB, Context: 8K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.2.

Arxiv:2507.13390 Base model:bharatgenai/param-1 Base model:finetune:bharatgena... Conversational Custom code Deploy:azure En Hi Instruct Parambharatgen Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/bharatgenai/Param-1-2.9B-Instruct

Param 1 2.9B Instruct Parameters and Internals

LLM Name	Param 1 2.9B Instruct
Repository 🤗	https://huggingface.co/bharatgenai/Param-1-2.9B-Instruct
Base Model(s)	bharatgenai/Param-1 bharatgenai/Param-1
Model Size	2.9b
Required VRAM	5.8 GB
Updated	2026-08-05
Maintainer	bharatgenai
Model Type	parambharatgen
Instruction-Based	Yes
Model Files	4.7 GB: 1-of-2 1.1 GB: 2-of-2
Supported Languages	hi en
Model Architecture	ParamBharatGenForCausalLM
License	apache-2.0
Context Length	8192
Model Max Length	8192
Transformers Version	4.56.2
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|endoftext\|>
Vocabulary Size	256011
Torch Data Type	float32

Rank the Param 1 2.9B Instruct 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 Param 1 2.9B Instruct

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Note: green Score (e.g. "73.2") means that the model is better than bharatgenai/Param-1-2.9B-Instruct.