🧠
Archived Kimi K2.5 Mlx 3.6bit model by inferencerlabs
⭐ 54.6
🧠
Model

Archived Kimi K2.5 Mlx 3.6bit

by inferencerlabs inferencerlabs/archived-kimi-k2.5-mlx-3.6bit
Free2AITools Nexus Index
54.6
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 47
P: Popularity 36
R: Recency 99
Q: Quality 50
Tech Context
3.6 Params
4.096K Ctx
Vital Performance
3.2K DL / 30D
Source β†’

Task categories from upstream metadata

πŸ’¬Chat & Dialogue
Low FNI signal 54.6 FNI Score
3.6B Params
4k Context
3.2K Downloads
8G GPU ~4GB Est. VRAM
Model Information Summary
Entity Passport
Registry ID inferencerlabs/archived-kimi-k2.5-mlx-3.6bit
Provider huggingface
πŸ’Ύ

Compute Threshold

~4GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{inferencerlabs_archived_kimi_k2_5_mlx_3_6bit,
  author = {inferencerlabs},
  title = {Archived Kimi K2.5 Mlx 3.6bit Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/inferencerlabs/archived-Kimi-K2.5-MLX-3.6bit}},
  note = {Accessed via Free2AITools.}
}
APA Style
inferencerlabs. (2026). Archived Kimi K2.5 Mlx 3.6bit [Model]. Free2AITools. https://huggingface.co/inferencerlabs/archived-Kimi-K2.5-MLX-3.6bit

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run archived-kimi-k2.5-mlx-3.6bit
πŸ€— HF Download
huggingface-cli download inferencerlabs/archived-kimi-k2.5-mlx-3.6bit

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 47
Popularity (P) 36
Recency (R) 99
Quality (Q) 50

πŸ’¬ Index Insight

FNI V2.0 for Archived Kimi K2.5 Mlx 3.6bit: Authority (A:47), Popularity (P:36), Recency (R:99), Quality (Q:50). 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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πŸš€ What's Next?

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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.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

HuggingFace Hub
3.2KDownloads
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--inferencerlabs--archived-kimi-k2.5-mlx-3.6bit
slug
inferencerlabs--archived-kimi-k2.5-mlx-3.6bit
source
huggingface
author
inferencerlabs
license
tags
mlx, quantized, text-generation, en, base_model:moonshotai/kimi-k2.5, base_model:finetune:moonshotai/kimi-k2.5, region:us

βš™οΈ Technical Specs

architecture
null
params billions
3.6
context length
4,096
pipeline tag
text-generation
vram gb
4
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
3,239
stars
null
forks
null

Data indexed from public sources. Updated daily.