What are the hardware requirements for MNLP M2 Rag Model?

MNLP M2 Rag Model 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 M2 Rag Model and how large is it?

MNLP M2 Rag Model is developed by Portgas37, 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 M2 Rag Model?

MNLP M2 Rag Model is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

MNLP M2 Rag Model by Portgas37 — VRAM 2.4GB, 32K context

Name: MNLP M2 Rag Model
Author: Portgas37

MNLP M2 Rag Model is an open-source language model by Portgas37. Features: 596m LLM, VRAM: 2.4GB, Context: 32K, LLM Explorer Score: 0.18.

Arxiv:1910.09700 Conversational Endpoints compatible Qwen3 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/Portgas37/MNLP_M2_rag_model

MNLP M2 Rag Model Parameters and Internals

LLM Name	MNLP M2 Rag Model
Repository 🤗	https://huggingface.co/Portgas37/MNLP_M2_rag_model
Model Size	596m
Required VRAM	2.4 GB
Updated	2026-07-05
Maintainer	Portgas37
Model Type	qwen3
Model Files	2.4 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	float32
Errors	replace

Rank the MNLP M2 Rag Model 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 M2 Rag Model

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Qwen Ye	40K / 1.2 GB	262	1
S1 Mini Sv	40K / 0.6 GB	106	1
S1 Mini MLX Bf16	40K / 1.2 GB	218	1
Test6	40K / 1.2 GB	15	0
... Calling Qwen0.6B 27jul Merged	40K / 1.2 GB	670	0
...Qwen0.6B 27jul Merged Llamacpp	40K / 1.2 GB	5	0
Sam Reason A1	40K / 2.4 GB	20	1
Qwen3 0p6mimo S3 Bf161200	32K / 1.2 GB	117	0
Qwen3 Grpo2 96	32K / 1.2 GB	5	0
Qwen3 0p6 Bf16 600	32K / 1.2 GB	6	0

Note: green Score (e.g. "73.2") means that the model is better than Portgas37/MNLP_M2_rag_model.