What are the hardware requirements for SmolLM2 FT Eng?

SmolLM2 FT Eng 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 Eng and how large is it?

SmolLM2 FT Eng is developed by wilka, 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 Eng?

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

SmolLM2 FT Eng by wilka — VRAM 0.5GB, 8K context

Name: SmolLM2 FT Eng
Author: wilka

SmolLM2 FT Eng is an open-source language model by wilka. Features: 135m LLM, VRAM: 0.5GB, Context: 8K, License: proprietary, LLM Explorer Score: 0.17.

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

Model Card on HF 🤗: https://huggingface.co/wilka/SmolLM2-FT-eng

SmolLM2 FT Eng Parameters and Internals

LLM Name	SmolLM2 FT Eng
Repository 🤗	https://huggingface.co/wilka/SmolLM2-FT-eng
Model Name	SmolLM2-FT-eng
Base Model(s)	SmolLM2 135M HuggingFaceTB/SmolLM2-135M
Model Size	135m
Required VRAM	0.5 GB
Updated	2025-06-22
Maintainer	wilka
Model Type	llama
Model Files	0.5 GB 0.0 GB
Gated Model	Yes
Model Architecture	LlamaForCausalLM
License	proprietary
Context Length	8192
Model Max Length	8192
Transformers Version	4.51.0.dev0
Tokenizer Class	GPT2Tokenizer
Padding Token	<\|im_end\|>
Vocabulary Size	49152
Torch Data Type	float32

Rank the SmolLM2 FT Eng 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

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... Subtitle Postprocessor Merged	8K / 0.5 GB	16	1
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SmolLM2 135M Mofbandgap	8K / 0.3 GB	7	0

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