🧠
Userrepgen 4b model by microsoft
⭐ 49.1
🧠
Model

Userrepgen 4b

by microsoft microsoft/userrepgen-4b
Free2AITools Nexus Index
49.1
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 41
P: Popularity 0
R: Recency 100
Q: Quality 65
Tech Context
4.54 Params
32.768K Ctx
Vital Performance —
Source β†’

Task categories from upstream metadata

πŸ’¬Chat & Dialogue

Technical Constraints

Experimental / High Latency
Low FNI signal 49.1 FNI Score
4.54B Params
32k Context
0 Downloads
8G GPU ~6GB Est. VRAM
Dense QWEN3_5FORCONDITIONALGENERATION Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID microsoft/userrepgen-4b
License MIT
Provider huggingface
πŸ’Ύ

Compute Threshold

~5.9GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{microsoft_userrepgen_4b,
  author = {microsoft},
  title = {Userrepgen 4b Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/microsoft/UserRepGen-4B}},
  note = {Accessed via Free2AITools.}
}
APA Style
microsoft. (2026). Userrepgen 4b [Model]. Free2AITools. https://huggingface.co/microsoft/UserRepGen-4B

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run userrepgen-4b
πŸ€— HF Download
huggingface-cli download microsoft/userrepgen-4b
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) 41
Popularity (P) 0
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Userrepgen 4b: Authority (A:41), Popularity (P:0), Recency (R:100), 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
---

πŸš€ What's Next?

πŸ“Š

Models in this category

Browse models in the same category

⚑

Deployment reference

Understand deployment options

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.
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--microsoft--userrepgen-4b
slug
microsoft--userrepgen-4b
source
huggingface
author
microsoft
license
MIT
tags
transformers, safetensors, qwen3_5, image-text-to-text, user-representation, user-simulation, simulation, multi-turn, text-generation, conversational, en, base_model:qwen/qwen3.5-4b, base_model:finetune:qwen/qwen3.5-4b, license:mit, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
Qwen3_5ForConditionalGeneration
params billions
4.54
context length
32,768
pipeline tag
text-generation
vram gb
5.9
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.