Instructions to use flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ")
model = AutoModelForCausalLM.from_pretrained("flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ

SGLang

How to use flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ 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 "flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ" \
    --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": "flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ",
		"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 "flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ" \
        --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": "flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ with Docker Model Runner:
```
docker model run hf.co/flashvenom/Airoboros-13B-SuperHOT-8K-4bit-GPTQ
```

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Check out the documentation for more information.

Model upload of Airoboros-13B-SuperHOT in 4-bit GPTQ version, converted using GPTQ-for-LLaMa; Source model from https://huggingface.co/Peeepy/Airoboros-13b-SuperHOT-8k.

This uses the Airoboros-13B(v1.2) model and applies the SuperHOT 8K LoRA on top, allowing for improved coherence at larger context lenghts, as well as improving output quality of Airoboros to be more verbose.

You will need a monkey-patch at inference to use the 8k context, please see patch file present, if you are using a different inference engine (like llama.cpp / exllama) you will need to add the monkey patch there.

Note: If you are using exllama the monkey-patch is built into the engine, please use -cpe to set the scaling factor, ie. if you are running it at 4k context, pass `-cpe 2 -l 4096`

Patch file present in repo or can be accessed here: https://huggingface.co/kaiokendev/superhot-13b-8k-no-rlhf-test/raw/main/llama_rope_scaled_monkey_patch.py

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This uses the Airoboros-13B(v1.2) model and applies the SuperHOT 8K LoRA on top, allowing for improved coherence at larger context lenghts, as well as improving output quality of Airoboros to be more verbose.

Note: If you are using exllama the monkey-patch is built into the engine, please use -cpe to set the scaling factor, ie. if you are running it at 4k context, pass -cpe 2 -l 4096

Note: If you are using exllama the monkey-patch is built into the engine, please use -cpe to set the scaling factor, ie. if you are running it at 4k context, pass `-cpe 2 -l 4096`