What are the hardware requirements for QCammel 70 X?

QCammel 70 X requires approximately 138 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 QCammel 70 X and how large is it?

QCammel 70 X is developed by augtoma. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does QCammel 70 X perform on standard benchmarks?

QCammel 70 X has the following published scores: MMLU 70.18. Compare against reference models on this page or on the LLM Explorer leaderboards.

QCammel 70 X by augtoma — VRAM 138GB, 4K context

Name: QCammel 70 X
Author: augtoma

QCammel 70 X is an open-source language model by augtoma. Features: LLM, VRAM: 138GB, Context: 4K, License: other, LLM Explorer Score: 0.09, Arc: 68.3, HellaSwag: 87.9, MMLU: 70.2, GSM8K: 29.7.

Arxiv:2302.70971 Arxiv:2305.12031 Arxiv:2305.14314 En Llama Llama2 Pytorch Qcammel-70 Region:us Sharded

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

QCammel 70 X Parameters and Internals

Model Type

text generation

Use Cases

Applications:

academic medical knowledge, instruction-following capabilities

Training Details

Data Volume:

15,000 instructions

Methodology:

Distilled dataset using QLoRA

Model Architecture:

Auto-regressive language model using a decoder only transformer architecture.

Input Output

Input Format:

text only

Output Format:

text only

LLM Name	QCammel 70 X
Repository 🤗	https://huggingface.co/augtoma/qCammel-70-x
Required VRAM	138 GB
Updated	2026-08-05
Maintainer	augtoma
Model Type	llama
Model Files	9.8 GB: 1-of-15 9.8 GB: 2-of-15 10.0 GB: 3-of-15 9.8 GB: 4-of-15 9.8 GB: 5-of-15 9.8 GB: 6-of-15 10.0 GB: 7-of-15 9.8 GB: 8-of-15 9.8 GB: 9-of-15 9.8 GB: 10-of-15 10.0 GB: 11-of-15 9.8 GB: 12-of-15 9.8 GB: 13-of-15 9.5 GB: 14-of-15 0.5 GB: 15-of-15
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	other
Context Length	4096
Model Max Length	4096
Transformers Version	4.32.0.dev0
Tokenizer Class	LlamaTokenizer
Beginning of Sentence Token	<s>
End of Sentence Token	</s>
Unk Token	<unk>
Vocabulary Size	32000
Torch Data Type	float16

Rank the QCammel 70 X 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

Quantized Models of the QCammel 70 X

Model	Likes	Downloads	VRAM
QCammel 70 X GGUF	4	301	29 GB
QCammel 70 X AWQ	1	6	36 GB
QCammel 70 X GGML	3	10	28 GB
QCammel 70 X GPTQ	2	10	35 GB

Best Alternatives to QCammel 70 X

Best Alternatives	Context / RAM	Downloads	Likes
LWM Text 512K	512K / 13.5 GB	13	2
LWM Text Chat 512K	512K / 13.5 GB	9	2
LWM Text Chat 256K	256K / 13.5 GB	10	3
LWM Text 256K	256K / 13.5 GB	5	3
Pallas 0.5 LASER 0.1	195K / 68.9 GB	52	2
SnorTTS Indic V0	128K / 9.5 GB	518	22
Orpheus AUS	128K / GB	18	0
Finetuning Health Ci	128K / 6.5 GB	23	2
Ukr Synth Phi 3.5	128K / 7.6 GB	9	0
WizardLM Phi 3.5	128K / 7.6 GB	8	0

Note: green Score (e.g. "73.2") means that the model is better than augtoma/qCammel-70-x.