What are the hardware requirements for Aaditya Llama3 OpenBioLLM 8B AWQ 4bit Smashed?

Aaditya Llama3 OpenBioLLM 8B AWQ 4bit Smashed 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 Aaditya Llama3 OpenBioLLM 8B AWQ 4bit Smashed and how large is it?

Aaditya Llama3 OpenBioLLM 8B AWQ 4bit Smashed is developed by PrunaAI, a model with 8b 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 Aaditya Llama3 OpenBioLLM 8B AWQ 4bit Smashed?

Aaditya Llama3 OpenBioLLM 8B AWQ 4bit Smashed is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Aaditya Llama3 OpenBioLLM 8B AWQ 4bit Smashed by PrunaAI — VRAM 5.8GB, 8K context

Name: Aaditya Llama3 OpenBioLLM 8B AWQ 4bit Smashed
Author: PrunaAI

Aaditya Llama3 OpenBioLLM 8B AWQ 4bit Smashed is an open-source language model by PrunaAI. Features: 8b LLM, VRAM: 5.8GB, Context: 8K, Quantized, LLM Explorer Score: 0.12.

4-bit 4bit Awq Base model:aaditya/llama3-open... Base model:quantized:aaditya/l... Llama Pruna-ai Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/PrunaAI/aaditya-Llama3-OpenBioLLM-8B-AWQ-4bit-smashed

Aaditya Llama3 OpenBioLLM 8B AWQ 4bit Smashed Parameters and Internals

Model Type

compression

Use Cases

Areas:

Research, Commercial applications

Primary Use Cases:

Efficient AI model deployment

Considerations:

Efficiency tested under specific hardware and configurations. Recommend testing in your own environment for validation.

Input Output

Accepted Modalities:

text

Performance Tips:

Run the efficiency tests in application-specific settings for accurate measurements.

LLM Name	Aaditya Llama3 OpenBioLLM 8B AWQ 4bit Smashed
Repository 🤗	https://huggingface.co/PrunaAI/aaditya-Llama3-OpenBioLLM-8B-AWQ-4bit-smashed
Base Model(s)	Llama3 OpenBioLLM 8B aaditya/Llama3-OpenBioLLM-8B
Model Size	8b
Required VRAM	5.8 GB
Updated	2025-10-07
Maintainer	PrunaAI
Model Type	llama
Model Files	4.7 GB: 1-of-2 1.1 GB: 2-of-2
AWQ Quantization	Yes
Quantization Type	awq\|4bit
Model Architecture	LlamaForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.40.0
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|end_of_text\|>
Vocabulary Size	128256
Torch Data Type	float16

Rank the Aaditya Llama3 OpenBioLLM 8B AWQ 4bit Smashed 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

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...8B Instruct Gradient 1048K AWQ	1024K / 5.8 GB	8	0
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... Instruct 8B 32K V0.1 4bit AWQ	64K / 5.8 GB	9	0
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Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	6	0
Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	563	0
Meta Llama 3 8B Instruct AWQ	8K / 5.8 GB	7	0
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Note: green Score (e.g. "73.2") means that the model is better than PrunaAI/aaditya-Llama3-OpenBioLLM-8B-AWQ-4bit-smashed.