Instructions to use sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0 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 sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0 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 sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0:Q8_0 # Run inference directly in the terminal: llama cli -hf sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0:Q8_0 # Run inference directly in the terminal: llama cli -hf sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0:Q8_0
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 sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0:Q8_0
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 sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0:Q8_0
Use Docker
docker model run hf.co/sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0:Q8_0
- LM Studio
- Jan
- Ollama
How to use sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0 with Ollama:
ollama run hf.co/sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0:Q8_0
- Unsloth Studio
How to use sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0 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 sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0 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 sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0 to start chatting
- Docker Model Runner
How to use sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0 with Docker Model Runner:
docker model run hf.co/sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0:Q8_0
- Lemonade
How to use sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0:Q8_0
Run and chat with the model
lemonade run user.HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0-Q8_0
List all available models
lemonade list
- Atomic Chat
llama.cpp๋ฅผ ์ฌ์ฉํด gguf๋ก ๋ณํํ์ต๋๋ค.
from llama_cpp import Llama
llm = Llama(
model_path="HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-q8_0.gguf",
n_gpu_layers=-1,
main_gpu=0,
n_ctx=2048
)
output = llm(
"์ฌ๋ฏธ์๋ ์ด์ผ๊ธฐ ํ๋ ๋ง๋ค์ด์ค. 1000์ ์ด์์ด์ด์ผ ํด. ์์:", # Prompt
max_tokens=2048,
echo=True,
)
print(output)
geforce 3070 RTX๋ก ํ ์คํธํ์ผ๋ฉฐ, ์ฑ๋ฅ์ ๋ค์๊ณผ ๊ฐ์ต๋๋ค.
q8_0, peak: 1.8G
llama_perf_context_print: load time = 186.23 ms
llama_perf_context_print: prompt eval time = 186.15 ms / 19 tokens ( 9.80 ms per token, 102.07 tokens per second)
llama_perf_context_print: eval time = 3141.36 ms / 536 runs ( 5.86 ms per token, 170.63 tokens per second)
llama_perf_context_print: total time = 3758.06 ms / 555 tokens
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