Instructions to use sal076/L3.1_RP_test2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sal076/L3.1_RP_test2 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sal076/L3.1_RP_test2", dtype="auto")

llama-cpp-python

How to use sal076/L3.1_RP_test2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sal076/L3.1_RP_test2",
	filename="unsloth.F16.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 sal076/L3.1_RP_test2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sal076/L3.1_RP_test2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sal076/L3.1_RP_test2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sal076/L3.1_RP_test2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sal076/L3.1_RP_test2: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 sal076/L3.1_RP_test2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sal076/L3.1_RP_test2: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 sal076/L3.1_RP_test2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sal076/L3.1_RP_test2:Q4_K_M

Use Docker

docker model run hf.co/sal076/L3.1_RP_test2:Q4_K_M

LM Studio
Jan
Ollama
How to use sal076/L3.1_RP_test2 with Ollama:
```
ollama run hf.co/sal076/L3.1_RP_test2:Q4_K_M
```

Unsloth Studio new

How to use sal076/L3.1_RP_test2 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 sal076/L3.1_RP_test2 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 sal076/L3.1_RP_test2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sal076/L3.1_RP_test2 to start chatting

Docker Model Runner
How to use sal076/L3.1_RP_test2 with Docker Model Runner:
```
docker model run hf.co/sal076/L3.1_RP_test2:Q4_K_M
```

Lemonade

How to use sal076/L3.1_RP_test2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sal076/L3.1_RP_test2:Q4_K_M

Run and chat with the model

lemonade run user.L3.1_RP_test2-Q4_K_M

List all available models

lemonade list

Uploaded model

Developed by: sal076
License: llama 3.1
Finetuned from model : unsloth/meta-llama-3.1-8b-bnb-4bit

This a shit fintune quickly made as a proof of concept, This isn't supposed to be a useable model

Here is a updated better version, use this instead

Q4_K_M: https://huggingface.co/sal076/L3.1_RP_TEST3-Q4_K_M-GGUF

Q5_K_M: https://huggingface.co/sal076/L3.1_RP_TEST3-Q5_K_M-GGUF

Downloads last month: 36

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Inference Providers NEW

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sal076
/

L3.1_RP_test2

Uploaded model

Dataset used to train sal076/L3.1_RP_test2