Instructions to use StatPan/42dot_LLM-PLM-1.3B_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use StatPan/42dot_LLM-PLM-1.3B_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="StatPan/42dot_LLM-PLM-1.3B_GGUF",
	filename="ggml-model-f32.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use StatPan/42dot_LLM-PLM-1.3B_GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf StatPan/42dot_LLM-PLM-1.3B_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf StatPan/42dot_LLM-PLM-1.3B_GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf StatPan/42dot_LLM-PLM-1.3B_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf StatPan/42dot_LLM-PLM-1.3B_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 StatPan/42dot_LLM-PLM-1.3B_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf StatPan/42dot_LLM-PLM-1.3B_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 StatPan/42dot_LLM-PLM-1.3B_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf StatPan/42dot_LLM-PLM-1.3B_GGUF:Q4_K_M

Use Docker

docker model run hf.co/StatPan/42dot_LLM-PLM-1.3B_GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use StatPan/42dot_LLM-PLM-1.3B_GGUF with Ollama:
```
ollama run hf.co/StatPan/42dot_LLM-PLM-1.3B_GGUF:Q4_K_M
```

Unsloth Studio new

How to use StatPan/42dot_LLM-PLM-1.3B_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 StatPan/42dot_LLM-PLM-1.3B_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 StatPan/42dot_LLM-PLM-1.3B_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for StatPan/42dot_LLM-PLM-1.3B_GGUF to start chatting

Docker Model Runner
How to use StatPan/42dot_LLM-PLM-1.3B_GGUF with Docker Model Runner:
```
docker model run hf.co/StatPan/42dot_LLM-PLM-1.3B_GGUF:Q4_K_M
```

Lemonade

How to use StatPan/42dot_LLM-PLM-1.3B_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull StatPan/42dot_LLM-PLM-1.3B_GGUF:Q4_K_M

Run and chat with the model

lemonade run user.42dot_LLM-PLM-1.3B_GGUF-Q4_K_M

List all available models

lemonade list

42dot_LLM-PLM-1.3B_GGUF

모델 만든 사람: 42dot
원본 모델: 42dot_LLM-PLM-1.3B

설명

42dot 모델의 GGUF 경량화 모델을 만들어 뒀습니다.

42dot_LLM-PLM-1.3B

파일

링크에 연결 해두었으니 필요하신 분은 하트 주고 챙겨 가세요. gguf 원본 파일

42dot_LLM-PLM-1.3B.gguf

Q4, Q8 경량화 파일

이외 모델은 근본 없어서 올릴까 하다가 안 올리려고 합니다.

사용법

원본

원본 링크에서 사용 법을 확인하세요.

Llama.cpp로 사용법 샘플

For simple inferencing, use a command similar to

./main -m gguf-q4_k_m.gguf --temp 0 --top-k 4 --prompt "who was Joseph Weizenbaum?"

Llama.cpp로 토크나이징 샘플

To get a list of tokens, use a command similar to

./tokenization -m gguf-q4_k_m.gguf --prompt "who was Joseph Weizenbaum?"

Llama.cpp로 임베딩 샘플

Text embeddings are calculated with a command similar to

./embedding -m gguf-q4_k_m.gguf --prompt "who was Joseph Weizenbaum?"

License

원본 모델 라이센스는 Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) 참고하세요.

Downloads last month: 42

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

32-bit

View +1 variant

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support