🧠
JapanRailwaysimLLM model by galakenya
⭐ 37.5
🧠
Model

JapanRailwaysimLLM

by galakenya galakenya/japanrailwaysimllm
Free2AITools Nexus Index
37.5
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 13
R: Recency 97
Q: Quality 65
Tech Context
8 Params
8.192K Ctx
Vital Performance
205 DL / 30D
Source β†’

Task categories from upstream metadata

πŸ’¬Chat & Dialogue

Technical Constraints

Experimental / High Latency
Low FNI signal 37.5 FNI Score
8B Params
8k Context
205 Downloads
8G GPU ~8GB Est. VRAM
Dense GEMMA4FORCONDITIONALGENERATION Architecture
Restricted GEMMA License
Model Information Summary
Entity Passport
Registry ID galakenya/japanrailwaysimllm
License Gemma
Provider huggingface
πŸ’Ύ

Compute Threshold

~7.3GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{galakenya_japanrailwaysimllm,
  author = {galakenya},
  title = {JapanRailwaysimLLM Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/galakenya/JapanRailwaysimLLM}},
  note = {Accessed via Free2AITools.}
}
APA Style
galakenya. (2026). JapanRailwaysimLLM [Model]. Free2AITools. https://huggingface.co/galakenya/JapanRailwaysimLLM

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run japanrailwaysimllm
πŸ€— HF Download
huggingface-cli download galakenya/japanrailwaysimllm
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) 13
Recency (R) 97
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for JapanRailwaysimLLM: Authority (A:0), Popularity (P:13), 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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⚠️ Incomplete Data

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

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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
205Downloads
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--galakenya--japanrailwaysimllm
slug
galakenya--japanrailwaysimllm
source
huggingface
author
galakenya
license
Gemma
tags
gguf, japan, railway, train, 鉄道, ζ—₯本, ja, base_model:elyza/llama-3-elyza-jp-8b, base_model:quantized:elyza/llama-3-elyza-jp-8b, license:llama3, endpoints_compatible, region:us, conversational, transformers, safetensors, gemma4, image-text-to-text, text-generation-inference, unsloth, en, base_model:unsloth/gemma-4-e4b-unsloth-bnb-4bit, license:apache-2.0, text-generation, gemma, gemma-cpp, license:gemma

βš™οΈ Technical Specs

architecture
Gemma4ForConditionalGeneration
params billions
8
context length
8,192
pipeline tag
text-generation
vram gb
7.3
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
205

Data indexed from public sources. Updated daily.