Instructions to use Henk717/airochronos-33B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Henk717/airochronos-33B with Transformers:

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

pipe = pipeline("text-generation", model="Henk717/airochronos-33B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Henk717/airochronos-33B")
model = AutoModelForCausalLM.from_pretrained("Henk717/airochronos-33B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Henk717/airochronos-33B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Henk717/airochronos-33B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Henk717/airochronos-33B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Henk717/airochronos-33B

SGLang

How to use Henk717/airochronos-33B 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 "Henk717/airochronos-33B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Henk717/airochronos-33B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Henk717/airochronos-33B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Henk717/airochronos-33B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Henk717/airochronos-33B with Docker Model Runner:
```
docker model run hf.co/Henk717/airochronos-33B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

After the initial experiment with chronoboros-33B it was evident that the merge was to unpredictable to be useful, testing the individual models it became clear that the bias should be weighted towards Chronos. This is the new release of the merge with 75% chronos 33B, and 25% airoboros-1.4 33B.

Model has been tested with the Alpaca prompting format combined with KoboldAI Lite's instruct and chat modes, as well as regular story writing. It has also been tested on basic reasoning tasks, but has not seen much testing for factual information.

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Safetensors

Model size

33B params

Tensor type

F32

F16

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Quantizations

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