What are the hardware requirements for Slim Summary?

Slim Summary requires approximately 5.6 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Slim Summary and how large is it?

Slim Summary is developed by llmware, a model with 3b 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 Slim Summary?

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

Slim Summary by llmware — VRAM 5.6GB, 4K context

Name: Slim Summary
Author: llmware

Slim Summary is an open-source language model by llmware. Features: 3b LLM, VRAM: 5.6GB, Context: 4K, License: cc-by-sa-4.0, LLM Explorer Score: 0.11.

Custom code Pytorch Region:us Stablelm epoch

Model Card on HF 🤗: https://huggingface.co/llmware/slim-summary

Slim Summary Parameters and Internals

Model Type

text summarization

Use Cases

Primary Use Cases:

Summarize function-calls, generating output consisting of a python list of distinct summary points.

Additional Notes

The model has an experimental feature where an optional list size can be specified in the parameters, guiding the model to generate a specific number of summary points.

Training Details

Methodology:

The model is fine-tuned on top of llmware/bling-stable-lm-3b-4e1t-v0, which itself is a fine-tune of stabilityai/stablelm-3b-4elt.

Input Output

Input Format:

Text passage

Accepted Modalities:

text

Output Format:

A list of the form: ['summary_point1', 'summary_point2', 'summary_point3']

Performance Tips:

Use the 'quantized tool' version for fast inference. Use the automatic conversion handler from llmware to handle conversion to a Python list.

LLM Name	Slim Summary
Repository 🤗	https://huggingface.co/llmware/slim-summary
Model Size	3b
Required VRAM	5.6 GB
Updated	2026-07-31
Maintainer	llmware
Model Type	stablelm_epoch
Model Files	5.6 GB
Model Architecture	StableLMEpochForCausalLM
License	cc-by-sa-4.0
Context Length	4096
Model Max Length	4096
Transformers Version	4.33.2
Tokenizer Class	GPTNeoXTokenizer
Vocabulary Size	50304
Torch Data Type	bfloat16

Rank the Slim Summary 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 Slim Summary

Best Alternatives	Context / RAM	Downloads	Likes
Stable Code 3B Mlx	16K / 5.6 GB	17	1
Aura 3B	4K / 5.6 GB	3	2
Slim Extract	4K / 5.6 GB	10	13
Slim Boolean	4K / 5.6 GB	9	4
Slim Sa Ner	4K / 5.6 GB	20	6
Slim Tags 3B	4K / 5.6 GB	11	4
Slim Xsum	4K / 5.6 GB	14	6
Tofu 3B	4K / 5.6 GB	5	2
Memphis CoT 3B	4K / 5.6 GB	24	31
Fett Uccine Mini 3B	4K / 5.6 GB	6	3

Note: green Score (e.g. "73.2") means that the model is better than llmware/slim-summary.