🧠
Octen Embedding 0.6b Gguf model by cstr
⭐ 38.6
🧠
Model

Octen Embedding 0.6b Gguf

by cstr hf-model--cstr--octen-embedding-0.6b-gguf
Nexus Index
38.6 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 9
R: Recency 97
Q: Quality 50
Tech Context
0.6B Params
4.096K Ctx
Vital Performance
117 DL / 30D
0.0%
Source →
Audited 38.6 FNI Score
Tiny 0.6B Params
4k Context
117 Downloads
8G GPU ~2GB Est. VRAM
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID hf-model--cstr--octen-embedding-0.6b-gguf
License Apache-2.0
Provider huggingface
💾

Compute Threshold

~1.8GB VRAM

Interactive
Analyze Hardware
▼

* Static estimation for 4-Bit Quantization.

📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__cstr__octen_embedding_0.6b_gguf,
  author = {cstr},
  title = {Octen Embedding 0.6b Gguf Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/cstr/octen-embedding-0.6b-gguf}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
cstr. (2026). Octen Embedding 0.6b Gguf [Model]. Free2AITools. https://huggingface.co/cstr/octen-embedding-0.6b-gguf

🔬Technical Deep Dive

Full Specifications [+]

Quick Commands

🦙 Ollama Run
ollama run octen-embedding-0.6b-gguf
🤗 HF Download
huggingface-cli download cstr/octen-embedding-0.6b-gguf
📦 Install Lib
pip install -U transformers

⚖️ Nexus Index V2.0

Methodology Index Protocol
38.6
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 9
Recency (R) 97
Quality (Q) 50

💬 Index Insight

FNI V2.0 for Octen Embedding 0.6b Gguf: Semantic (S:50), Authority (A:0), Popularity (P:9), Recency (R:97), Quality (Q:50).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
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🚀 What's Next?

📊

Find Training Datasets

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Compare Benchmarks

See how this model ranks on standard tests

⚡

Deployment Guide

Understand deployment options

Technical Deep Dive

Octen-Embedding-0.6B — GGUF

GGUF conversion of Octen/Octen-Embedding-0.6B for use with CrispEmbed.

Model Details

  • Architecture: Qwen3 decoder with GQA (16 Q heads, 8 KV heads, head_dim=128)
  • Parameters: 0.6B (28 layers, 1024 hidden, 3072 intermediate)
  • Embedding dim: 1024
  • Pooling: Last-token
  • Tokenizer: GPT-2 BPE (151K vocab)
  • RoPE: theta=1,000,000
  • License: Apache-2.0

Files

File Type Size CosSim vs HF
octen-0.6b-f32.gguf F32 2.3 GB 0.9999
octen-0.6b-q8_0.gguf Q8_0 609 MB 0.9993
octen-0.6b-q4_k.gguf Q4_K 325 MB 0.9570

Usage with CrispEmbed

bash 
./crispembed -m octen-0.6b-q8_0.gguf "Hello world"
# prints 1024-dim L2-normalized embedding

# Server mode
./crispembed-server -m octen-0.6b-q8_0.gguf --port 8080
curl -X POST http://localhost:8080/embed -d '{"texts": ["Hello world"]}'

Conversion

Converted from the original PyTorch model using models/convert-decoder-embed-to-gguf.py from the CrispEmbed repo. Verified bit-identical (cos≥0.999) to HuggingFace sentence-transformers output.

⚠️ 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 →

📝 Limitations & Considerations

  • • Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • • VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • • FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

HuggingFace Hub
117Downloads
Hub Discussions
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

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

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--cstr--octen-embedding-0.6b-gguf
slug
cstr--octen-embedding-0.6b-gguf
source
huggingface
author
cstr
license
Apache-2.0
tags
sentence-transformers, gguf, feature-extraction, crispembed, qwen3, multilingual, base_model:octen/octen-embedding-0.6b, base_model:quantized:octen/octen-embedding-0.6b, license:apache-2.0, endpoints_compatible, region:us

⚙️ Technical Specs

architecture
null
params billions
0.6
context length
4,096
pipeline tag
feature-extraction
vram gb
1.8
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads
117
stars
0
forks
0

Data indexed from public sources. Updated daily.