Nomic/Dense

Nnomic-embed-text-v1.5 100M

chat
0.1B
Parameters
8K
Context length
1
Benchmarks
6
Quantizations
9.9M
HF downloads
Architecture
Dense
Released
2024-02-10
Layers
12
KV Heads
12
Head Dim
64
Family
embedding

Quantization Options

QuantBitsVRAMQuality
Q4_K_M4.890.5 GBgood
Q5_K_S5.570.6 GBgood
Q5_K_M5.70.6 GBgood
Q6_K6.560.6 GBexcellent
Q8_08.50.6 GBlossless
FP16160.7 GBlossless

Select your GPU above to see speed estimates and compatibility for each quantization.

READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN NOMIC-EMBED-TEXT-V1.5 100M NOW

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Community Ratings

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Benchmarks (1)

MMLU-PRO62.3

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run embedding:0b-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

Find the best GPU for nomic-embed-text-v1.5 100M

Build Hardware for nomic-embed-text-v1.5 100M
▸ SPEC SHEET

nomic-embed-text-v1.5 100M0.1B Dense.

▸ SPECIFICATIONS
PARAMETERS
0.1B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
8K tokens
CAPABILITIES
chat
RELEASE DATE
2024-02-10
PROVIDER
Nomic
FAMILY
embedding
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.890.5 GB94%
Q5_K_S5.570.6 GB96%
Q5_K_M5.70.6 GB96%
Q6_K6.560.6 GB97%
Q8_08.50.6 GB100%
FP16160.7 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO62.3
§ 02RUN COMMAND

Run nomic-embed-text-v1.5 100M locally with Ollama — needs 0.5 GB VRAM at Q4_K_M:

$ollama run embedding:0b