Instructions to use inetnuc/llama-3-8b-chat-nuclear-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inetnuc/llama-3-8b-chat-nuclear-lora with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("inetnuc/llama-3-8b-chat-nuclear-lora", dtype="auto")

llama-cpp-python

How to use inetnuc/llama-3-8b-chat-nuclear-lora with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="inetnuc/llama-3-8b-chat-nuclear-lora",
	filename="unsloth.Q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use inetnuc/llama-3-8b-chat-nuclear-lora with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf inetnuc/llama-3-8b-chat-nuclear-lora:Q8_0
# Run inference directly in the terminal:
llama-cli -hf inetnuc/llama-3-8b-chat-nuclear-lora:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf inetnuc/llama-3-8b-chat-nuclear-lora:Q8_0
# Run inference directly in the terminal:
llama-cli -hf inetnuc/llama-3-8b-chat-nuclear-lora:Q8_0

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 inetnuc/llama-3-8b-chat-nuclear-lora:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf inetnuc/llama-3-8b-chat-nuclear-lora:Q8_0

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 inetnuc/llama-3-8b-chat-nuclear-lora:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf inetnuc/llama-3-8b-chat-nuclear-lora:Q8_0

Use Docker

docker model run hf.co/inetnuc/llama-3-8b-chat-nuclear-lora:Q8_0

LM Studio
Jan
Ollama
How to use inetnuc/llama-3-8b-chat-nuclear-lora with Ollama:
```
ollama run hf.co/inetnuc/llama-3-8b-chat-nuclear-lora:Q8_0
```

Unsloth Studio

How to use inetnuc/llama-3-8b-chat-nuclear-lora 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 inetnuc/llama-3-8b-chat-nuclear-lora 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 inetnuc/llama-3-8b-chat-nuclear-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for inetnuc/llama-3-8b-chat-nuclear-lora to start chatting

Docker Model Runner
How to use inetnuc/llama-3-8b-chat-nuclear-lora with Docker Model Runner:
```
docker model run hf.co/inetnuc/llama-3-8b-chat-nuclear-lora:Q8_0
```

Lemonade

How to use inetnuc/llama-3-8b-chat-nuclear-lora with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull inetnuc/llama-3-8b-chat-nuclear-lora:Q8_0

Run and chat with the model

lemonade run user.llama-3-8b-chat-nuclear-lora-Q8_0

List all available models

lemonade list

LLAMA-3 8B Chat Nuclear Model

Developed by: inetnuc
License: apache-2.0
Finetuned from model: unsloth/llama-3-8b-bnb-4bit

This LLAMA-3 model was finetuned to enhance capabilities in text generation for nuclear-related topics. The training was accelerated using Unsloth and Huggingface's TRL library, achieving a 2x faster performance.

Finetuning Process

The model was finetuned using the Unsloth library, leveraging its efficient training capabilities. The process included the following steps:

Data Preparation: Loaded and preprocessed nuclear-related data.
Model Loading: Utilized unsloth/llama-3-8b-bnb-4bit as the base model.
LoRA Patching: Applied LoRA (Low-Rank Adaptation) for efficient training.
Training: Finetuned the model using Hugging Face's TRL library with optimized hyperparameters.

Model Details

Base Model: unsloth/llama-3-8b-bnb-4bit
Language: English (en)
License: Apache-2.0

Usage

Loading the Model

You can load the model and tokenizer using the following code snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("inetnuc/llama-3-8b-chat-nuclear-lora")
model = AutoModelForCausalLM.from_pretrained("inetnuc/llama-3-8b-chat-nuclear-lora")

# Example of generating text
inputs = tokenizer("what is the iaea approach for cyber security?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 8

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

8-bit

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