What are the hardware requirements for MNLP M3 Mcqa Model SimpleVal M1 Cot?

MNLP M3 Mcqa Model SimpleVal M1 Cot requires approximately 2.4 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 SimpleVal M1 Cot and how large is it?

MNLP M3 Mcqa Model SimpleVal M1 Cot is developed by shulijia, a model with 0.6b 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 SimpleVal M1 Cot?

MNLP M3 Mcqa Model SimpleVal M1 Cot 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 SimpleVal M1 Cot by shulijia — VRAM 2.4GB, 32K context

Name: MNLP M3 Mcqa Model SimpleVal M1 Cot
Author: shulijia

MNLP M3 Mcqa Model SimpleVal M1 Cot is an open-source language model by shulijia. Features: 0.6b LLM, VRAM: 2.4GB, Context: 32K, LLM Explorer Score: 0.18.

Base model:finetune:qwen/qwen3... Base model:qwen/qwen3-0.6b-bas... Conversational Endpoints compatible Generated from trainer Qwen3 Region:us Safetensors Sft Tensorboard Trl

Model Card on HF 🤗: https://huggingface.co/shulijia/MNLP_M3_mcqa_model_simpleVal_m1_cot

MNLP M3 Mcqa Model SimpleVal M1 Cot Parameters and Internals

LLM Name	MNLP M3 Mcqa Model SimpleVal M1 Cot
Repository 🤗	https://huggingface.co/shulijia/MNLP_M3_mcqa_model_simpleVal_m1_cot
Model Name	MNLP_M3_mcqa_model_simpleVal_m1_cot
Base Model(s)	Qwen/Qwen3-0.6B-Base Qwen/Qwen3-0.6B-Base
Model Size	0.6b
Required VRAM	2.4 GB
Updated	2026-07-21
Maintainer	shulijia
Model Type	qwen3
Model Files	2.4 GB 0.0 GB
Model Architecture	Qwen3ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.52.2
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	float32
Errors	replace

Rank the MNLP M3 Mcqa Model SimpleVal M1 Cot 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

Best Alternatives to MNLP M3 Mcqa Model SimpleVal M1 Cot

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S1 Mini	40K / 1.5 GB	1136	180
Meridian Ac Nano	40K / 1.2 GB	412	1
Qwen3 Reranker 0.6B	40K / 1.2 GB	2508313	377
Quipu 0.6B	40K / 1.2 GB	351	4
Qwen3 0.6B FP8	40K / 1.1 GB	1581403	62
Privasis Cleaner 0.6B	40K / 1.2 GB	1171	18
Clary 0.6 0.6B	40K / 1.2 GB	88	1
Qwen3 Finetuned	40K / 1.2 GB	98	1
Privasis Cleaner 0.6B	40K / 1.2 GB	76	0
Qwen3 0.6B Sushi Code Expert	40K / 1.5 GB	40	0

Note: green Score (e.g. "73.2") means that the model is better than shulijia/MNLP_M3_mcqa_model_simpleVal_m1_cot.