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Clean Eval Rft W2c Same 4 model by agurung
⭐ 37.5
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Model

Clean Eval Rft W2c Same 4

by agurung agurung/clean-eval-rft-w2c-same-4
Free2AITools Nexus Index
37.5
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 0
R: Recency 100
Q: Quality 50
Tech Context
32.768K Ctx
Vital Performance —
Source β†’

Task categories from upstream metadata

πŸ’¬Chat & Dialogue

Technical Constraints

Experimental / High Latency
Low FNI signal 37.5 FNI Score
Tiny - Params
32k Context
0 Downloads
Dense QWEN3FORCAUSALLM Architecture
Model Information Summary
Entity Passport
Registry ID agurung/clean-eval-rft-w2c-same-4
Provider huggingface
πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{agurung_clean_eval_rft_w2c_same_4,
  author = {agurung},
  title = {Clean Eval Rft W2c Same 4 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/agurung/clean-eval-rft-w2c-same-4}},
  note = {Accessed via Free2AITools.}
}
APA Style
agurung. (2026). Clean Eval Rft W2c Same 4 [Model]. Free2AITools. https://huggingface.co/agurung/clean-eval-rft-w2c-same-4

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ€— HF Download
huggingface-cli download agurung/clean-eval-rft-w2c-same-4
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) 0
Recency (R) 100
Quality (Q) 50

πŸ’¬ Index Insight

FNI V2.0 for Clean Eval Rft W2c Same 4: Authority (A:0), Popularity (P:0), Recency (R:100), 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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⚠️ 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.
  • ⚠ License Unknown: Verify licensing terms before commercial use.
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--agurung--clean-eval-rft-w2c-same-4
slug
agurung--clean-eval-rft-w2c-same-4
source
huggingface
author
agurung
tags
transformers, safetensors, qwen3, text-generation, generated_from_trainer, diversity, uniform, trl, sft, cobalt, conversational, dataset:cobalt-traces-rft_w2c_same_4, base_model:qwen/qwen3-4b-instruct-2507, base_model:finetune:qwen/qwen3-4b-instruct-2507, text-generation-inference, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
Qwen3ForCausalLM
context length
32,768
pipeline tag
text-generation

πŸ“Š Engagement & Metrics

downloads
0

Data indexed from public sources. Updated daily.