🧠
Kimi K2.5 Mxfp4 Autoround model by INCModel
⭐ 39.6
🧠
Model

Kimi K2.5 Mxfp4 Autoround

by INCModel incmodel/kimi-k2.5-mxfp4-autoround
Free2AITools Nexus Index
39.6
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 10
R: Recency 96
Q: Quality 65
Tech Context
2.99 Params
4.096K Ctx
Vital Performance
125 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 39.6 FNI Score
Tiny 2.99B Params
4k Context
125 Downloads
8G GPU ~4GB Est. VRAM
Dense KIMIK25FORCONDITIONALGENERATION Architecture
Restricted OTHER License
Model Information Summary
Entity Passport
Registry ID incmodel/kimi-k2.5-mxfp4-autoround
License Other
Provider huggingface
πŸ’Ύ

Compute Threshold

~3.5GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{incmodel_kimi_k2_5_mxfp4_autoround,
  author = {INCModel},
  title = {Kimi K2.5 Mxfp4 Autoround Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/INCModel/Kimi-K2.5-MXFP4-AutoRound}},
  note = {Accessed via Free2AITools.}
}
APA Style
INCModel. (2026). Kimi K2.5 Mxfp4 Autoround [Model]. Free2AITools. https://huggingface.co/INCModel/Kimi-K2.5-MXFP4-AutoRound

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run kimi-k2.5-mxfp4-autoround
πŸ€— HF Download
huggingface-cli download incmodel/kimi-k2.5-mxfp4-autoround
πŸ“¦ Install Lib
pip install -U transformers

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0
Popularity (P) 10
Recency (R) 96
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Kimi K2.5 Mxfp4 Autoround: Authority (A:0), Popularity (P:10), Recency (R:96), 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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πŸš€ What's Next?

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

Social Proof

HuggingFace Hub
125Downloads
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--incmodel--kimi-k2.5-mxfp4-autoround
slug
incmodel--kimi-k2.5-mxfp4-autoround
source
huggingface
author
INCModel
license
Other
tags
transformers, safetensors, kimi_k25, feature-extraction, auto-round, image-text-to-text, conversational, custom_code, arxiv:2309.05516, base_model:moonshotai/kimi-k2.5, base_model:quantized:moonshotai/kimi-k2.5, license:other, 8-bit, compressed-tensors, region:us

βš™οΈ Technical Specs

architecture
KimiK25ForConditionalGeneration
params billions
2.99
context length
4,096
pipeline tag
image-text-to-text
vram gb
3.5
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
125
stars
0
forks
0

Data indexed from public sources. Updated daily.