What are the hardware requirements for QCammel 70 X GGUF?

QCammel 70 X GGUF requires approximately 29.3 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed QCammel 70 X GGUF and how large is it?

QCammel 70 X GGUF is developed by TheBloke. 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 QCammel 70 X GGUF?

QCammel 70 X GGUF is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

QCammel 70 X GGUF by TheBloke — VRAM 29.3GB

Name: QCammel 70 X GGUF
Author: TheBloke

QCammel 70 X GGUF is an open-source language model by TheBloke. Features: LLM, VRAM: 29.3GB, License: other, Quantized, LLM Explorer Score: 0.09.

Arxiv:2302.70971 Arxiv:2305.12031 Arxiv:2305.14314 Base model:augtoma/qcammel-70-... Base model:quantized:augtoma/q... En Gguf Llama Llama2 Pytorch Qcammel-70 Quantized Region:us

Model Card on HF 🤗: https://huggingface.co/TheBloke/qCammel-70-x-GGUF

QCammel 70 X GGUF Parameters and Internals

Model Type

llama

Additional Notes

The model is fine-tuned for academic medical knowledge and instruction-following capabilities. It's based on Llama-2 70B model and trained on a distilled dataset of 15,000 instructions.

Training Details

Methodology:

QLoRA

Model Architecture:

Decoder-only transformer architecture based on Llama 2

Input Output

Input Format:

Text

Output Format:

Text

LLM Name	QCammel 70 X GGUF
Repository 🤗	https://huggingface.co/TheBloke/qCammel-70-x-GGUF
Model Name	qCammel 70
Model Creator	augtoma
Base Model(s)	QCammel 70 X augtoma/qCammel-70-x
Required VRAM	29.3 GB
Updated	2026-07-15
Maintainer	TheBloke
Model Type	llama
Model Files	29.3 GB 36.1 GB 33.2 GB 29.9 GB 38.9 GB 41.4 GB 39.1 GB 47.5 GB 48.8 GB 47.5 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	AutoModel
License	other

Rank the QCammel 70 X GGUF 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 TheBloke/qCammel-70-x-GGUF.