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Deepseek V4 Flash Jangtq4 model by Jangq Ai
⭐ 38.8
🧠
Model

Deepseek V4 Flash Jangtq4

by Jangq Ai jangq-ai/deepseek-v4-flash-jangtq4
Free2AITools Nexus Index
38.8
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 0
R: Recency 76
Q: Quality 65
Tech Context
37.92 Params
4.096K Ctx
Vital Performance —
Source β†’

Task categories from upstream metadata

πŸ’¬Chat & Dialogue

Technical Constraints

Experimental / High Latency
Low FNI signal 38.8 FNI Score
37.92B Params
4k Context
0 Downloads
H100+ ~31GB Est. VRAM
Dense DEEPSEEKV4FORCAUSALLM Architecture
Restricted OTHER License
Model Information Summary
Entity Passport
Registry ID jangq-ai/deepseek-v4-flash-jangtq4
License Other
Provider huggingface
πŸ’Ύ

Compute Threshold

~30.9GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{jangq_ai_deepseek_v4_flash_jangtq4,
  author = {Jangq Ai},
  title = {Deepseek V4 Flash Jangtq4 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/JANGQ-AI/DeepSeek-V4-Flash-JANGTQ4}},
  note = {Accessed via Free2AITools.}
}
APA Style
Jangq Ai. (2026). Deepseek V4 Flash Jangtq4 [Model]. Free2AITools. https://huggingface.co/JANGQ-AI/DeepSeek-V4-Flash-JANGTQ4

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run deepseek-v4-flash-jangtq4
πŸ€— HF Download
huggingface-cli download jangq-ai/deepseek-v4-flash-jangtq4

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) 0
Recency (R) 76
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Deepseek V4 Flash Jangtq4: Authority (A:0), Popularity (P:0), Recency (R:76), 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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⚠️ 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.
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--jangq-ai--deepseek-v4-flash-jangtq4
slug
jangq-ai--deepseek-v4-flash-jangtq4
source
huggingface
author
Jangq Ai
license
Other
tags
mlx, safetensors, deepseek_v4, jang, jangtq, jangtq4, deepseek, moe, mla, mhc, apple-silicon, 4bit, codebook, turboquant, text-generation, base_model:deepseek-ai/deepseek-v4-flash, license:other, region:us

βš™οΈ Technical Specs

architecture
DeepseekV4ForCausalLM
params billions
37.92
context length
4,096
pipeline tag
text-generation
vram gb
30.9
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 2GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
0

Data indexed from public sources. Updated daily.