Instructions to use refuelai/Llama-3-Refueled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use refuelai/Llama-3-Refueled with Transformers:

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

pipe = pipeline("text-generation", model="refuelai/Llama-3-Refueled")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("refuelai/Llama-3-Refueled")
model = AutoModelForCausalLM.from_pretrained("refuelai/Llama-3-Refueled")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use refuelai/Llama-3-Refueled with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/refuelai/Llama-3-Refueled

SGLang

How to use refuelai/Llama-3-Refueled 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 "refuelai/Llama-3-Refueled" \
    --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": "refuelai/Llama-3-Refueled",
		"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 "refuelai/Llama-3-Refueled" \
        --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": "refuelai/Llama-3-Refueled",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use refuelai/Llama-3-Refueled with Docker Model Runner:
```
docker model run hf.co/refuelai/Llama-3-Refueled
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Details

RefuelLLM-2-small, aka Llama-3-Refueled, is a Llama3-8B base model instruction tuned on a corpus of 2750+ datasets, spanning tasks such as classification, reading comprehension, structured attribute extraction and entity resolution. We're excited to open-source the model for the community to build on top of.

More details about RefuelLLM-2 family of models
You can also try out the models in our LLM playground

Model developers - Refuel AI

Input - Text only.

Output - Text only.

Architecture - Llama-3-Refueled is built on top of Llama-3-8B-instruct which is an auto-regressive language model that uses an optimized transformer architecture.

Release Date - May 8, 2024.

License - CC BY-NC 4.0

How to use

This repository contains weights for Llama-3-Refueled that are compatible for use with HuggingFace. See the snippet below for usage with Transformers:

>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer

>>> model_id = "refuelai/Llama-3-Refueled"
>>> tokenizer = AutoTokenizer.from_pretrained(model_id)
>>> model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

>>> messages = [{"role": "user", "content": "Is this comment toxic or non-toxic: RefuelLLM is the new way to label text data!"}]

>>> inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda")

>>> outputs = model.generate(inputs, max_new_tokens=20)
>>> print(tokenizer.decode(outputs[0]))

Training Data

The model was both trained on over 4 Billion tokens, spanning 2750+ NLP tasks. Our training collection consists majorly of:

Human annotated datasets like Flan, Task Source, and the Aya collection
Synthetic datasets like OpenOrca, OpenHermes and WizardLM
Proprietary datasets developed or licensed by Refuel AI

Benchmarks

In this section, we report the results for Refuel models on our benchmark of labeling tasks. For details on the methodology see here.

Provider	Model	LLM Output Quality (by task type)
		Overall	Classification	Reading Comprehension	Structure Extraction	Entity Matching
Refuel	RefuelLLM-2	83.82%	84.94%	76.03%	88.16%	92.00%
OpenAI	GPT-4-Turbo	80.88%	81.77%	72.08%	84.79%	97.20%
Refuel	RefuelLLM-2-small (Llama-3-Refueled)	79.67%	81.72%	70.04%	84.28%	92.00%
Anthropic	Claude-3-Opus	79.19%	82.49%	67.30%	88.25%	94.96%
Meta	Llama3-70B-Instruct	78.20%	79.38%	66.03%	85.96%	94.13%
Google	Gemini-1.5-Pro	74.59%	73.52%	60.67%	84.27%	98.48%
Mistral	Mixtral-8x7B-Instruct	62.87%	79.11%	45.56%	47.08%	86.52%
Anthropic	Claude-3-Sonnet	70.99%	79.91%	45.44%	78.10%	96.34%
Anthropic	Claude-3-Haiku	69.23%	77.27%	50.19%	84.97%	54.08%
OpenAI	GPT-3.5-Turbo	68.13%	74.39%	53.21%	69.40%	80.41%
Meta	Llama3-8B-Instruct	62.30%	68.52%	49.16%	65.09%	63.61%

Limitations

The Llama-3-Refueled does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.

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