What are the hardware requirements for MNLP M3 Mcqa Model?

MNLP M3 Mcqa Model requires approximately 1.2 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed MNLP M3 Mcqa Model and how large is it?

MNLP M3 Mcqa Model is developed by cam-1000, a model with 596m 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 MNLP M3 Mcqa Model?

MNLP M3 Mcqa Model is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

MNLP M3 Mcqa Model by cam-1000 — VRAM 1.2GB, 32K context

Name: MNLP M3 Mcqa Model
Author: cam-1000

MNLP M3 Mcqa Model is an open-source language model by cam-1000. Features: 596m LLM, VRAM: 1.2GB, Context: 32K, LLM Explorer Score: 0.18.

Qwen3 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/cam-1000/MNLP_M3_mcqa_model

MNLP M3 Mcqa Model Parameters and Internals

LLM Name	MNLP M3 Mcqa Model
Repository 🤗	https://huggingface.co/cam-1000/MNLP_M3_mcqa_model
Model Size	596m
Required VRAM	1.2 GB
Updated	2026-08-07
Maintainer	cam-1000
Model Type	qwen3
Model Files	1.2 GB
Model Architecture	Qwen3ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.52.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the MNLP M3 Mcqa Model capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
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Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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MNLP M3 Rag Model	32K / 2.4 GB	10	0

Note: green Score (e.g. "73.2") means that the model is better than cam-1000/MNLP_M3_mcqa_model.