What are the hardware requirements for Llama 2 7B Chat Hf 2bitgs8 Hqq?

Llama 2 7B Chat Hf 2bitgs8 Hqq requires approximately 3.8 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 2 7B Chat Hf 2bitgs8 Hqq and how large is it?

Llama 2 7B Chat Hf 2bitgs8 Hqq is developed by mobiuslabsgmbh, 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 Llama 2 7B Chat Hf 2bitgs8 Hqq?

Llama 2 7B Chat Hf 2bitgs8 Hqq is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 2 7B Chat Hf 2bitgs8 Hqq by mobiuslabsgmbh — VRAM 3.8GB, 4K context

Name: Llama 2 7B Chat Hf 2bitgs8 Hqq
Author: mobiuslabsgmbh

Llama 2 7B Chat Hf 2bitgs8 Hqq is an open-source language model by mobiuslabsgmbh. Features: 7b LLM, VRAM: 3.8GB, Context: 4K, License: llama2, LLM Explorer Score: 0.12.

Conversational Llama Region:us

Model Card on HF 🤗: https://huggingface.co/mobiuslabsgmbh/Llama-2-7b-chat-hf_2bitgs8_hqq

Llama 2 7B Chat Hf 2bitgs8 Hqq Parameters and Internals

Model Type

text generation

Use Cases

Areas:

research, text generation

Applications:

language modeling, chatbot solutions

Primary Use Cases:

text generation tasks, understanding specific prompts

Limitations:

quantization may affect precision for certain tasks

Additional Notes

This version offloads the meta-data to the CPU for efficient VRAM usage with low-rank adapters.

Training Details

Data Sources:

wikitext-2-raw-v1, timdettmers/openassistant-guanaco, microsoft/orca-math-word-problems-200k, meta-math/MetaMathQA, HuggingFaceH4/ultrafeedback_binarized

Methodology:

Quantized at 2-bit, fine-tuned with low-rank adapter (HQQ+)

Model Architecture:

Quantized with low-rank adapter

Input Output

Input Format:

text prompts

Accepted Modalities:

text

Output Format:

generated text responses

Performance Tips:

Use streaming inference for efficient text generation

LLM Name	Llama 2 7B Chat Hf 2bitgs8 Hqq
Repository 🤗	https://huggingface.co/mobiuslabsgmbh/Llama-2-7b-chat-hf_2bitgs8_hqq
Model Size	7b
Required VRAM	3.8 GB
Updated	2026-08-06
Maintainer	mobiuslabsgmbh
Model Type	llama
Model Files	3.8 GB
Model Architecture	LlamaForCausalLM
License	llama2
Context Length	4096
Model Max Length	4096
Transformers Version	4.39.0.dev0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float16

Rank the Llama 2 7B Chat Hf 2bitgs8 Hqq 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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Note: green Score (e.g. "73.2") means that the model is better than mobiuslabsgmbh/Llama-2-7b-chat-hf_2bitgs8_hqq.