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Kev 0.6b model by jaredpalmer
⭐ 39
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Model

Kev 0.6b

by jaredpalmer jaredpalmer/kev-0.6b
Free2AITools Nexus Index
39.0
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 44
P: Popularity 31
R: Recency 97
Q: Quality 65
Tech Context
0.6B Params
4.096K Ctx
Vital Performance
1.9K DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 39 FNI Score
Tiny 0.6B Params
4k Context
1.9K Downloads
8G GPU ~2GB Est. VRAM
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID jaredpalmer/kev-0.6b
License Apache-2.0
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.8GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{jaredpalmer_kev_0_6b,
  author = {jaredpalmer},
  title = {Kev 0.6b Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/jaredpalmer/kev-0.6b}},
  note = {Accessed via Free2AITools.}
}
APA Style
jaredpalmer. (2026). Kev 0.6b [Model]. Free2AITools. https://huggingface.co/jaredpalmer/kev-0.6b

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run kev-0.6b
πŸ€— HF Download
huggingface-cli download jaredpalmer/kev-0.6b

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 44
Popularity (P) 31
Recency (R) 97
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Kev 0.6b: Authority (A:44), Popularity (P:31), Recency (R:97), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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Deployment reference

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Technical Deep Dive

⚠️ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source β†’

πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.

Social Proof

HuggingFace Hub
1.9KDownloads
Hub Discussions
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Updated daily

Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-model--jaredpalmer--kev-0.6b
slug
jaredpalmer--kev-0.6b
source
huggingface
author
jaredpalmer
license
Apache-2.0
tags
peft, safetensors, decision-model, calibration, lora, multiple-choice, typesafe, research-preview, text-classification, en, dataset:legacy-datasets/banking77, dataset:google/boolq, dataset:fancyzhx/ag_news, dataset:nyu-mll/multi_nli, dataset:setfit/sst5, dataset:yelp/yelp_review_full, dataset:cogcomp/trec, dataset:fancyzhx/dbpedia_14, dataset:setfit/amazon_reviews_multi_en, dataset:stanfordnlp/imdb, base_model:qwen/qwen3-0.6b-base, base_model:adapter:qwen/qwen3-0.6b-base, license:apache-2.0, mod

βš™οΈ Technical Specs

params billions
0.6
context length
4,096
pipeline tag
text-classification
vram gb
1.8
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
1,872

Data indexed from public sources. Updated daily.