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Convert Awq W4a16 Asym E2e model by Nm Testing
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Model

Convert Awq W4a16 Asym E2e

by Nm Testing hf-model--nm-testing--convert_awq_w4a16_asym-e2e
Free2AITools Nexus Index
39.0 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 100
Q: Quality 65
Tech Context
0.64B Params
32.768K Ctx
Vital Performance
0 DL / 30D
0.0%
Source →
Audited 39 FNI Score
Tiny 0.64B Params
32k Context
0 Downloads
8G GPU ~3GB Est. VRAM
Dense QWEN2FORCAUSALLM Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID hf-model--nm-testing--convert_awq_w4a16_asym-e2e
License Apache-2.0
Provider huggingface
💾

Compute Threshold

~3GB VRAM

Interactive
Analyze Hardware
â–ŧ

* Static estimation for 4-Bit Quantization.

📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__nm_testing__convert_awq_w4a16_asym_e2e,
  author = {Nm Testing},
  title = {Convert Awq W4a16 Asym E2e Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/nm-testing/convert_awq_w4a16_asym-e2e}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Nm Testing. (2026). Convert Awq W4a16 Asym E2e [Model]. Free2AITools. https://huggingface.co/nm-testing/convert_awq_w4a16_asym-e2e

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run convert_awq_w4a16_asym-e2e
🤗 HF Download
huggingface-cli download nm-testing/convert_awq_w4a16_asym-e2e
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Free2AITools Nexus Index V2.0

Methodology Index Protocol
Semantic (S) 50
Authority (A) 0
Popularity (P) 0
Recency (R) 100
Quality (Q) 65

đŸ’Ŧ Index Insight

FNI V2.0 for Convert Awq W4a16 Asym E2e: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:100), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
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Technical Deep Dive

⚠ī¸ 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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📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--nm-testing--convert_awq_w4a16_asym-e2e
slug
nm-testing--convert_awq_w4a16_asym-e2e
source
huggingface
author
Nm Testing
license
Apache-2.0
tags
transformers, safetensors, qwen2, text-generation, chat, conversational, en, arxiv:2407.10671, base_model:qwen/qwen2.5-0.5b-instruct, base_model:quantized:qwen/qwen2.5-0.5b-instruct, license:apache-2.0, text-generation-inference, endpoints_compatible, compressed-tensors, region:us

⚙ī¸ Technical Specs

architecture
Qwen2ForCausalLM
params billions
0.64
context length
32,768
pipeline tag
text-generation
vram gb
3
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 2GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads
0
stars
0
forks
null

Data indexed from public sources. Updated daily.