Instructions to use macadeliccc/airoboros-9b-3.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use macadeliccc/airoboros-9b-3.2 with Transformers:

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

pipe = pipeline("text-generation", model="macadeliccc/airoboros-9b-3.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("macadeliccc/airoboros-9b-3.2")
model = AutoModelForCausalLM.from_pretrained("macadeliccc/airoboros-9b-3.2", device_map="auto")
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 Settings

vLLM

How to use macadeliccc/airoboros-9b-3.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "macadeliccc/airoboros-9b-3.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "macadeliccc/airoboros-9b-3.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/macadeliccc/airoboros-9b-3.2

SGLang

How to use macadeliccc/airoboros-9b-3.2 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 "macadeliccc/airoboros-9b-3.2" \
    --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": "macadeliccc/airoboros-9b-3.2",
		"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 "macadeliccc/airoboros-9b-3.2" \
        --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": "macadeliccc/airoboros-9b-3.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use macadeliccc/airoboros-9b-3.2 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 macadeliccc/airoboros-9b-3.2 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 macadeliccc/airoboros-9b-3.2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for macadeliccc/airoboros-9b-3.2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="macadeliccc/airoboros-9b-3.2",
    max_seq_length=2048,
)

Docker Model Runner
How to use macadeliccc/airoboros-9b-3.2 with Docker Model Runner:
```
docker model run hf.co/macadeliccc/airoboros-9b-3.2
```

airoboros-9b-3.2

Prompt Template: ChatML

from openai import OpenAI
# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

system = """
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
{user}
ENDINSTRUCTION
"""

user = "What is the new color of blueberries?"
chat_response = client.chat.completions.create(
    model="macadeliccc/airoboros-9b-3.2",
    messages=[
        {"role": "system", "content": system},
        {"role": "user", "content": user},
    ]
)
print("Chat response:", chat_response)

Expected Output

Chat response: ChatCompletion(id='cmpl-6bce7c051ffd41878624683faea90719', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Blueberries are now green.', role='assistant', function_call=None, tool_calls=None))], created=273292, model='macadeliccc/airoboros-9b-3.2', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=7, prompt_tokens=119, total_tokens=126))

Developed by: macadeliccc
License: apache-2.0
Finetuned from model : 01-ai/Yi-9B

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

Downloads last month: 6

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for macadeliccc/airoboros-9b-3.2

Base model

01-ai/Yi-9B

Finetuned

(4)

this model

macadeliccc
/

airoboros-9b-3.2

airoboros-9b-3.2

Expected Output

Model tree for macadeliccc/airoboros-9b-3.2

Dataset used to train macadeliccc/airoboros-9b-3.2

Space using macadeliccc/airoboros-9b-3.2 1