Instructions to use mykor/llama-nemotron-embed-1b-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mykor/llama-nemotron-embed-1b-v2-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf mykor/llama-nemotron-embed-1b-v2-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mykor/llama-nemotron-embed-1b-v2-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mykor/llama-nemotron-embed-1b-v2-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mykor/llama-nemotron-embed-1b-v2-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf mykor/llama-nemotron-embed-1b-v2-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mykor/llama-nemotron-embed-1b-v2-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf mykor/llama-nemotron-embed-1b-v2-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mykor/llama-nemotron-embed-1b-v2-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mykor/llama-nemotron-embed-1b-v2-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mykor/llama-nemotron-embed-1b-v2-GGUF with Ollama:
ollama run hf.co/mykor/llama-nemotron-embed-1b-v2-GGUF:Q4_K_M
- Unsloth Studio
How to use mykor/llama-nemotron-embed-1b-v2-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mykor/llama-nemotron-embed-1b-v2-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mykor/llama-nemotron-embed-1b-v2-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mykor/llama-nemotron-embed-1b-v2-GGUF to start chatting
- Docker Model Runner
How to use mykor/llama-nemotron-embed-1b-v2-GGUF with Docker Model Runner:
docker model run hf.co/mykor/llama-nemotron-embed-1b-v2-GGUF:Q4_K_M
- Lemonade
How to use mykor/llama-nemotron-embed-1b-v2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mykor/llama-nemotron-embed-1b-v2-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.llama-nemotron-embed-1b-v2-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
nvidia/llama-nemotron-embed-1b-v2-GGUF
import numpy as np
import torch
from llama_cpp import Llama
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
"nvidia/llama-nemotron-embed-1b-v2",
trust_remote_code=True,
model_kwargs={"dtype": torch.bfloat16},
)
llama = Llama.from_pretrained(
repo_id="mykor/llama-nemotron-embed-1b-v2-GGUF",
filename="llama-nemotron-embed-1B-v2-BF16.gguf",
verbose=False,
embedding=True,
n_ctx=0,
)
text = """λ¨μμλ λλ₯Ό λ λ
λ ν¬λ―Έν΄μ§κ³
μ΄λ€ μΈλ‘μμ νκ³
μ΄μκ°λλΌλ
λ΄κ² λΏμλ κΈ°μ΅μ
λ μ°¬λνκ² λ²μ Έ
μμ§λ λ μ μμ§λ λͺ»νκ³ λ
μ λ λ λ μ κ°ν κ² κ°μλ°
κ²μ μ§λ¦° μ±λ‘
μ²μμ΄λΌμ κ·Έλ
κ° λ³Έ μ μλ κΈΈμ
λ μ μ μμ μ±
κ°μλ₯Ό μΈμ΄ μ±
μμ²λ κ½μμ²λΌ
λ¨μ΄μ§ λ λ΄ μμ μ‘μμ€λ
λμ λ°€μ μ§μΈκ²
λΉμΌλ‘
λ
λ΄ λ§μ ν κ·Έλ¦Όμλ λ μΌν€λ λ―νκ³
κΈ°λ€λ¦¬λ νμ΄μ λ
μ€μ³κ°λ― λ λκ³
κ²¨μ° λ§€λ¬λ¦° ν¬λ―Έν λ¬λΉμ λ―Έλλ¬μ Έ
μμ§λ λ λ²μ΄λμ§ λͺ»νκ³ λ
μ λ λ λ μ 무λμ Έ κ°λλ°
μ΄μ© μ€ λͺ¨λ₯΄κ³
μ²μμ΄λΌμ κ·Έλ
κ° λ³Έ μ μλ κΈΈμ
λ μ μ μμ μ±
κ°μλ₯Ό μΈμ΄ μ±
μμ²λ κ½μμ²λΌ
λ¨μ΄μ§ λ λ΄ μμ μ‘μμ€λ
λμ λ°€μ μ§μΈκ²
λΉμΌλ‘
κ·Έλ₯ λ¬μλμ μ λλ¨Έμ
μ΄λμμ§λ νλμ΄
μ°λ¦΄ λ€μ μ°Ύμ μ μκ²
κ²μ λ°€μ΄
무μμ§ μλ€κΈ°λ³΄λ¨
ν¬κΈ°ν μ μλ κ² μλ
μ΄μ μμΌ λ³΄μ΄λ€
μ΄λ―Έ λ°λ»νλλ°
κ³ κ°λ₯Ό λ¨κ΅¬κ³
λ κ·λ₯Ό λ§κ³
λ΄ κ³μ μ§μΌμ£Όλ
λ λμΉ λ λ νλ² μ‘μμ€λ
μ½μν κ² μ°¬λν λ―Έμλ₯Ό
OH~ OH~ OH~
(κ·Έλ₯ λ¬μλμ μ λλ¨Έμ
μ΄λμμ§λ νλμ΄
μ°λ¦΄ λ€μ μ°Ύμ μ μκ²
κ²μ λ°€μ΄
무μμ§ μλ€κΈ°λ³΄λ¨
ν¬κΈ°ν μ μλ κ² μλ)
μμΉ¨μΌ κ±°μΌ μ°λ¦° λ"""
embed1 = model.encode(text)
embed2 = np.array(llama.embed(text), dtype=np.float32)
print(cos_sim(embed1, embed2).item())
0.9999110698699951
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