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

Libraries

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

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="inetnuc/llama-3-8b-chat-nuclear")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inetnuc/llama-3-8b-chat-nuclear")
model = AutoModelForCausalLM.from_pretrained("inetnuc/llama-3-8b-chat-nuclear")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inetnuc/llama-3-8b-chat-nuclear"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inetnuc/llama-3-8b-chat-nuclear",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "inetnuc/llama-3-8b-chat-nuclear" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inetnuc/llama-3-8b-chat-nuclear",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "inetnuc/llama-3-8b-chat-nuclear" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inetnuc/llama-3-8b-chat-nuclear",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

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

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="inetnuc/llama-3-8b-chat-nuclear",
    max_seq_length=2048,
)

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

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

Files and Versions

File Name	Description
.gitattributes	Initial commit
README.md	Model description and usage
adapter_config.json	Configuration for adapter
adapter_model.safetensors	Finetuned model weights
config.json	Configuration for base model
generation_config.json	Generation configuration for model
model-00001-of-00007.safetensors	Part of the base model weights
model-00002-of-00007.safetensors	Part of the base model weights
model-00003-of-00007.safetensors	Part of the base model weights
model-00004-of-00007.safetensors	Part of the base model weights
model-00005-of-00007.safetensors	Part of the base model weights
model-00006-of-00007.safetensors	Part of the base model weights
model-00007-of-00007.safetensors	Part of the base model weights
model.safetensors.index.json	Index for the model weights
special_tokens_map.json	Special tokens mapping
tokenizer.json	Tokenizer data
tokenizer_config.json	Configuration for tokenizer

Model Card Authors

MUSTAFA UMUT OZBEK

Contact

https://www.linkedin.com/in/mustafaumutozbek/ https://x.com/m_umut_ozbek

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")
model = AutoModelForCausalLM.from_pretrained("inetnuc/llama-3-8b-chat-nuclear")

# 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: 30

Safetensors

Model size

8B params

Tensor type

F32

Model tree for inetnuc/llama-3-8b-chat-nuclear

Base model

meta-llama/Meta-Llama-3-8B

Quantized

unsloth/llama-3-8b-bnb-4bit

Finetuned

(3097)

this model