What are the hardware requirements for Digest Sft2?

Digest Sft2 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 Digest Sft2 and how large is it?

Digest Sft2 is developed by usr-wwelsh, 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 Digest Sft2?

Digest Sft2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Digest Sft2 by usr-wwelsh — VRAM 0.5GB, 8K context

Name: Digest Sft2
Author: usr-wwelsh

Digest Sft2 is an open-source language model by usr-wwelsh. Features: 135m LLM, VRAM: 0.5GB, Context: 8K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.26.

Base model:finetune:huggingfac... Base model:huggingfacetb/smoll... Conversational Endpoints compatible Generated from trainer Instruct Llama Region:us Safetensors Sft Trl

Model Card on HF 🤗: https://huggingface.co/usr-wwelsh/digest-sft2

Digest Sft2 Parameters and Internals

LLM Name	Digest Sft2
Repository 🤗	https://huggingface.co/usr-wwelsh/digest-sft2
Base Model(s)	SmolLM2 135M Instruct HuggingFaceTB/SmolLM2-135M-Instruct
Model Size	135m
Required VRAM	0.5 GB
Updated	2026-08-25
Maintainer	usr-wwelsh
Model Type	llama
Instruction-Based	Yes
Model Files	0.5 GB 0.0 GB
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	8192
Model Max Length	8192
Transformers Version	5.15.1
Tokenizer Class	GPT2Tokenizer
Padding Token	<\|im_end\|>
Vocabulary Size	49152
Errors	replace

Rank the Digest Sft2 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 Digest Sft2

Best Alternatives	Context / RAM	Downloads	Likes
SmolLM2 135M Instruct	8K / 0.3 GB	120	1
SmolLM2 135M Instruct	8K / 0.3 GB	1857297	388
SmolLM2 135M Sukshma	8K / GB	150	1
SmolLM2 135M Reasoning 5K	8K / 0.3 GB	276	3
... Subtitle Postprocessor Merged	8K / 0.5 GB	16	1
SmolLM2 135M Instruction Tuned	8K / 0.3 GB	10	0
SmolLM2 Rethink 135M	8K / 0.5 GB	6	2
...M2 135M Instruct Clinical Note	8K / 0.5 GB	9	1
SmolLM2 135M Instruct	8K / 0.3 GB	4887	3
SmolLM2 135M Instruct Ita	8K / 0.1 GB	24	0

Note: green Score (e.g. "73.2") means that the model is better than usr-wwelsh/digest-sft2.