Instructions to use sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16",
	filename="HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16:BF16
# Run inference directly in the terminal:
llama-cli -hf sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16:BF16
# Run inference directly in the terminal:
llama-cli -hf sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16:BF16

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-bf16:BF16
# Run inference directly in the terminal:
./llama-cli -hf sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16:BF16

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-bf16:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16:BF16

Use Docker

docker model run hf.co/sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16:BF16

LM Studio
Jan
Ollama
How to use sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16 with Ollama:
```
ollama run hf.co/sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16:BF16
```

Unsloth Studio

How to use sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16 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-bf16 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-bf16 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-bf16 to start chatting

Docker Model Runner
How to use sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16 with Docker Model Runner:
```
docker model run hf.co/sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16:BF16
```

Lemonade

How to use sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16:BF16

Run and chat with the model

lemonade run user.HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16-BF16

List all available models

lemonade list

llama.cpp를 사용해 gguf로 변환했습니다.

from llama_cpp import Llama

llm = Llama(
    model_path="HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16.gguf",
    n_gpu_layers=-1,
    main_gpu=0,
    n_ctx=2048
)

output = llm(
    "재미있는 이야기 하나 만들어줘. 1000자 이상이어야 해. 시작:", # Prompt
    max_tokens=2048,
    echo=True,

)
print(output)

geforce 3070 RTX로 테스트했으며, 성능은 다음과 같습니다.

bf16, peak: 4GB
llama_perf_context_print:        load time =     210.50 ms
llama_perf_context_print: prompt eval time =     210.42 ms /    19 tokens (   11.07 ms per token,    90.30 tokens per second)
llama_perf_context_print:        eval time =   17923.17 ms /  2028 runs   (    8.84 ms per token,   113.15 tokens per second)
llama_perf_context_print:       total time =   21307.79 ms /  2047 tokens

Downloads last month: 10

GGUF

Model size

2B params

Architecture

llama

Hardware compatibility

16-bit

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Model tree for sorryhyun/HyperCLOVAX-SEED-Text-Instruct-1.5B-gguf-bf16

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