🧠
Laser Qwen3 8b I1 Gguf model by mradermacher
⭐ 31.4
🧠
Model

Laser Qwen3 8b I1 Gguf

by mradermacher mradermacher/laser-qwen3-8b-i1-gguf
Free2AITools Nexus Index
31.4
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 33
P: Popularity 20
R: Recency 75
Q: Quality 65
Tech Context
8 Params
4.096K Ctx
Vital Performance
498 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 31.4 FNI Score
8B Params
4k Context
498 Downloads
8G GPU ~8GB Est. VRAM
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID mradermacher/laser-qwen3-8b-i1-gguf
License MIT
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{mradermacher_laser_qwen3_8b_i1_gguf,
  author = {mradermacher},
  title = {Laser Qwen3 8b I1 Gguf Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/mradermacher/LaSER-Qwen3-8B-i1-GGUF}},
  note = {Accessed via Free2AITools.}
}
APA Style
mradermacher. (2026). Laser Qwen3 8b I1 Gguf [Model]. Free2AITools. https://huggingface.co/mradermacher/LaSER-Qwen3-8B-i1-GGUF

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run laser-qwen3-8b-i1-gguf
πŸ€— HF Download
huggingface-cli download mradermacher/laser-qwen3-8b-i1-gguf
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) 33
Popularity (P) 20
Recency (R) 75
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Laser Qwen3 8b I1 Gguf: Authority (A:33), Popularity (P:20), Recency (R:75), 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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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.

Social Proof

HuggingFace Hub
498Downloads
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--mradermacher--laser-qwen3-8b-i1-gguf
slug
mradermacher--laser-qwen3-8b-i1-gguf
source
huggingface
author
mradermacher
license
MIT
tags
gguf, endpoints_compatible, region:us, imatrix, conversational, transformers, dense-retrieval, latent-reasoning, embeddings, information-retrieval, feature-extraction, en, dataset:jinjiajie/laser-training, base_model:alibaba-nlp/laser-qwen3-8b, base_model:quantized:alibaba-nlp/laser-qwen3-8b, license:mit

βš™οΈ Technical Specs

params billions
8
context length
4,096
pipeline tag
feature-extraction
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
498
stars
0
forks
0

Data indexed from public sources. Updated daily.