What are the hardware requirements for Aspera SWE Llama 7B Updated 4bit AWQ?

Aspera SWE Llama 7B Updated 4bit AWQ requires approximately 3.9 GB of VRAM and supports a context window of 16K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Aspera SWE Llama 7B Updated 4bit AWQ and how large is it?

Aspera SWE Llama 7B Updated 4bit AWQ is developed by agi-designer, a model with 7b 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 Aspera SWE Llama 7B Updated 4bit AWQ?

Aspera SWE Llama 7B Updated 4bit AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Aspera SWE Llama 7B Updated 4bit AWQ by agi-designer — VRAM 3.9GB, 16K context

Name: Aspera SWE Llama 7B Updated 4bit AWQ
Author: agi-designer

Aspera SWE Llama 7B Updated 4bit AWQ is an open-source language model by agi-designer. Features: 7b LLM, VRAM: 3.9GB, Context: 16K, Quantized, LLM Explorer Score: 0.13.

4-bit 4bit Autotrain compatible Awq Endpoints compatible Llama Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/agi-designer/Aspera-SWE-Llama-7b-updated-4bit-awq

Aspera SWE Llama 7B Updated 4bit AWQ Parameters and Internals

LLM Name	Aspera SWE Llama 7B Updated 4bit AWQ
Repository 🤗	https://huggingface.co/agi-designer/Aspera-SWE-Llama-7b-updated-4bit-awq
Base Model(s)	Aspera SWE Llama 7B Updated agi-designer/Aspera-SWE-Llama-7b-updated
Model Size	7b
Required VRAM	3.9 GB
Updated	2025-09-23
Maintainer	agi-designer
Model Type	llama
Model Files	3.9 GB
AWQ Quantization	Yes
Quantization Type	awq\|4bit
Model Architecture	LlamaForCausalLM
Context Length	16384
Model Max Length	16384
Transformers Version	4.41.2
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	float16

Rank the Aspera SWE Llama 7B Updated 4bit AWQ 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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