Instructions to use jeiku/General_Purpose_3B_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jeiku/General_Purpose_3B_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jeiku/General_Purpose_3B_GGUF",
	filename="General_Purpose_3B-Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use jeiku/General_Purpose_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 jeiku/General_Purpose_3B_GGUF:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf jeiku/General_Purpose_3B_GGUF:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jeiku/General_Purpose_3B_GGUF:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf jeiku/General_Purpose_3B_GGUF:Q4_K_S

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 jeiku/General_Purpose_3B_GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf jeiku/General_Purpose_3B_GGUF:Q4_K_S

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 jeiku/General_Purpose_3B_GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jeiku/General_Purpose_3B_GGUF:Q4_K_S

Use Docker

docker model run hf.co/jeiku/General_Purpose_3B_GGUF:Q4_K_S

LM Studio
Jan
Ollama
How to use jeiku/General_Purpose_3B_GGUF with Ollama:
```
ollama run hf.co/jeiku/General_Purpose_3B_GGUF:Q4_K_S
```

Unsloth Studio new

How to use jeiku/General_Purpose_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 jeiku/General_Purpose_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 jeiku/General_Purpose_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 jeiku/General_Purpose_3B_GGUF to start chatting

Docker Model Runner
How to use jeiku/General_Purpose_3B_GGUF with Docker Model Runner:
```
docker model run hf.co/jeiku/General_Purpose_3B_GGUF:Q4_K_S
```

Lemonade

How to use jeiku/General_Purpose_3B_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jeiku/General_Purpose_3B_GGUF:Q4_K_S

Run and chat with the model

lemonade run user.General_Purpose_3B_GGUF-Q4_K_S

List all available models

lemonade list

mooby

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the linear merge method.

Models Merged

The following models were included in the merge:

new1 + jeiku/Theory_of_Mind_128_StableLM
new1 + jeiku/Everything_v3_128_StableLM
new1 + jeiku/Gnosis_StableLM

Configuration

The following YAML configuration was used to produce this model:

merge_method: linear
models:
  - model: new1+jeiku/Theory_of_Mind_128_StableLM
    parameters:
      weight: 1
  - model: new1+jeiku/Everything_v3_128_StableLM
    parameters:
      weight: 1
  - model: new1+jeiku/Gnosis_StableLM
    parameters:
      weight: 1
dtype: float16

Downloads last month: 33

GGUF

Model size

3B params

Architecture

stablelm

Hardware compatibility

2-bit

4-bit

6-bit

16-bit

Inference Providers NEW

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Model tree for jeiku/General_Purpose_3B_GGUF

jeiku/Everything_v3_128_StableLM

jeiku/Theory_of_Mind_128_StableLM

Merge model

this model

Paper for jeiku/General_Purpose_3B_GGUF

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 8