Instructions to use RichardErkhov/vonPipe_-_jak2InstructSFT-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/vonPipe_-_jak2InstructSFT-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RichardErkhov/vonPipe_-_jak2InstructSFT-gguf",
	filename="jak2InstructSFT.IQ3_M.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 RichardErkhov/vonPipe_-_jak2InstructSFT-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RichardErkhov/vonPipe_-_jak2InstructSFT-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RichardErkhov/vonPipe_-_jak2InstructSFT-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 RichardErkhov/vonPipe_-_jak2InstructSFT-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RichardErkhov/vonPipe_-_jak2InstructSFT-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 RichardErkhov/vonPipe_-_jak2InstructSFT-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/vonPipe_-_jak2InstructSFT-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 RichardErkhov/vonPipe_-_jak2InstructSFT-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/vonPipe_-_jak2InstructSFT-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/vonPipe_-_jak2InstructSFT-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/vonPipe_-_jak2InstructSFT-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/vonPipe_-_jak2InstructSFT-gguf:Q4_K_M
```

Unsloth Studio new

How to use RichardErkhov/vonPipe_-_jak2InstructSFT-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 RichardErkhov/vonPipe_-_jak2InstructSFT-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 RichardErkhov/vonPipe_-_jak2InstructSFT-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RichardErkhov/vonPipe_-_jak2InstructSFT-gguf to start chatting

Docker Model Runner
How to use RichardErkhov/vonPipe_-_jak2InstructSFT-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/vonPipe_-_jak2InstructSFT-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/vonPipe_-_jak2InstructSFT-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/vonPipe_-_jak2InstructSFT-gguf:Q4_K_M

Run and chat with the model

lemonade run user.vonPipe_-_jak2InstructSFT-gguf-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

jak2InstructSFT - GGUF

Model creator: https://huggingface.co/vonPipe/
Original model: https://huggingface.co/vonPipe/jak2InstructSFT/

Name	Quant method	Size
jak2InstructSFT.Q2_K.gguf	Q2_K	2.96GB
jak2InstructSFT.IQ3_XS.gguf	IQ3_XS	3.28GB
jak2InstructSFT.IQ3_S.gguf	IQ3_S	3.43GB
jak2InstructSFT.Q3_K_S.gguf	Q3_K_S	3.41GB
jak2InstructSFT.IQ3_M.gguf	IQ3_M	3.52GB
jak2InstructSFT.Q3_K.gguf	Q3_K	3.74GB
jak2InstructSFT.Q3_K_M.gguf	Q3_K_M	3.74GB
jak2InstructSFT.Q3_K_L.gguf	Q3_K_L	4.03GB
jak2InstructSFT.IQ4_XS.gguf	IQ4_XS	4.18GB
jak2InstructSFT.Q4_0.gguf	Q4_0	4.34GB
jak2InstructSFT.IQ4_NL.gguf	IQ4_NL	4.38GB
jak2InstructSFT.Q4_K_S.gguf	Q4_K_S	4.37GB
jak2InstructSFT.Q4_K.gguf	Q4_K	4.58GB
jak2InstructSFT.Q4_K_M.gguf	Q4_K_M	4.58GB
jak2InstructSFT.Q4_1.gguf	Q4_1	4.78GB
jak2InstructSFT.Q5_0.gguf	Q5_0	5.21GB
jak2InstructSFT.Q5_K_S.gguf	Q5_K_S	5.21GB
jak2InstructSFT.Q5_K.gguf	Q5_K	5.34GB
jak2InstructSFT.Q5_K_M.gguf	Q5_K_M	5.34GB
jak2InstructSFT.Q5_1.gguf	Q5_1	5.65GB
jak2InstructSFT.Q6_K.gguf	Q6_K	6.14GB
jak2InstructSFT.Q8_0.gguf	Q8_0	7.95GB

Original model description:

base_model: vonPipe/selfiesPretrainInstruct tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en

Uploaded model

Developed by: vonPipe
License: apache-2.0
Finetuned from model : vonPipe/selfiesPretrainInstruct

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: -

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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