Instructions to use TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2")
model = AutoModelForMultimodalLM.from_pretrained("TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2")
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 TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2

SGLang

How to use TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2 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 "TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2" \
    --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": "TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2",
		"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 "TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2" \
        --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": "TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2
```

Kayllene 57B v1.0

exllamav2 quant for TeeZee/Kyllene-57B-v1.0

Runs smoothly on single 3090 in webui with context length set to 4096, ExLlamav2_HF loader and cache_8bit=True

All comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:

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Collection including TeeZee/Kyllene-57B-v1.0-bpw3.0-h6-exl2

24 GB VRAM

Collection

Quants that run fast on single 3090/4090 card with 24GB of VRAM and 4096 context length • 19 items • Updated Mar 21, 2025 • 7