What are the hardware requirements for Qwen Mcqa Custom Sft 50K Sft Focus NILS?

Qwen Mcqa Custom Sft 50K Sft Focus NILS 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 Qwen Mcqa Custom Sft 50K Sft Focus NILS and how large is it?

Qwen Mcqa Custom Sft 50K Sft Focus NILS is developed by coralieb7, 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 Qwen Mcqa Custom Sft 50K Sft Focus NILS?

Qwen Mcqa Custom Sft 50K Sft Focus NILS is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen Mcqa Custom Sft 50K Sft Focus NILS by coralieb7 — VRAM 2.4GB, 32K context

Name: Qwen Mcqa Custom Sft 50K Sft Focus NILS
Author: coralieb7

Qwen Mcqa Custom Sft 50K Sft Focus NILS is an open-source language model by coralieb7. 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/coralieb7/qwen_mcqa_custom_sft_50k_sft_focus-NILS

Qwen Mcqa Custom Sft 50K Sft Focus NILS Parameters and Internals

LLM Name	Qwen Mcqa Custom Sft 50k Sft Focus NILS
Repository 🤗	https://huggingface.co/coralieb7/qwen_mcqa_custom_sft_50k_sft_focus-NILS
Model Size	596m
Required VRAM	2.4 GB
Updated	2026-08-07
Maintainer	coralieb7
Model Type	qwen3
Model Files	2.4 GB
Model Architecture	Qwen3ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.51.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	float32
Errors	replace

Rank the Qwen Mcqa Custom Sft 50K Sft Focus NILS 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

Best Alternatives to Qwen Mcqa Custom Sft 50K Sft Focus NILS

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Qwen Ye	40K / 1.2 GB	262	1
S1 Mini MLX Bf16	40K / 1.2 GB	218	1
S1 Mini Sv	40K / 0.6 GB	106	1
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... 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 coralieb7/qwen_mcqa_custom_sft_50k_sft_focus-NILS.