What are the hardware requirements for SmolLM2 FT MyDataset?

SmolLM2 FT MyDataset requires approximately 0.5 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed SmolLM2 FT MyDataset and how large is it?

SmolLM2 FT MyDataset is developed by jianxun, a model with 135m 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 SmolLM2 FT MyDataset?

SmolLM2 FT MyDataset is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

SmolLM2 FT MyDataset by jianxun — VRAM 0.5GB, 8K context

Name: SmolLM2 FT MyDataset
Author: jianxun

SmolLM2 FT MyDataset is an open-source language model by jianxun. Features: 135m LLM, VRAM: 0.5GB, Context: 8K, LLM Explorer Score: 0.16.

Base model:finetune:huggingfac... Base model:huggingfacetb/smoll... Conversational Endpoints compatible Generated from trainer Llama Module 1 Region:us Safetensors Sft Smol-course Trl

Model Card on HF 🤗: https://huggingface.co/jianxun/SmolLM2-FT-MyDataset

SmolLM2 FT MyDataset Parameters and Internals

LLM Name	SmolLM2 FT MyDataset
Repository 🤗	https://huggingface.co/jianxun/SmolLM2-FT-MyDataset
Model Name	SmolLM2-FT-MyDataset
Base Model(s)	SmolLM2 135M HuggingFaceTB/SmolLM2-135M
Model Size	135m
Required VRAM	0.5 GB
Updated	2025-12-27
Maintainer	jianxun
Model Type	llama
Model Files	0.5 GB 0.0 GB
Model Architecture	LlamaForCausalLM
Context Length	8192
Model Max Length	8192
Transformers Version	4.46.3
Tokenizer Class	GPT2Tokenizer
Padding Token	<\|im_end\|>
Vocabulary Size	49152
Torch Data Type	float32

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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

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Note: green Score (e.g. "73.2") means that the model is better than jianxun/SmolLM2-FT-MyDataset.