What are the hardware requirements for Slim Sa Ner?

Slim Sa Ner 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 Sa Ner and how large is it?

Slim Sa Ner 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 Sa Ner?

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

Slim Sa Ner by llmware — VRAM 5.6GB, 4K context

Name: Slim Sa Ner
Author: llmware

Slim Sa Ner 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-sa-ner

Slim Sa Ner Parameters and Internals

Model Type

Sentiment Analysis, Named Entity Recognition

Additional Notes

This model showcases the combination of sentiment analysis and named entity recognition as function calls on a specialized decoder-based LLM.

Input Output

Input Format:

function = "classify" params = "sentiment, person, organization, place" prompt = " " + {text} + "\n" + "< {function} > " + {params} + "" + "\n:"

Accepted Modalities:

text

Output Format:

python dictionary

LLM Name	Slim Sa Ner
Repository 🤗	https://huggingface.co/llmware/slim-sa-ner
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 Sa Ner 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 Sa Ner

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 Tags 3B	4K / 5.6 GB	11	4
Slim Summary	4K / 5.6 GB	10	8
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-sa-ner.