Instructions to use EldritchLabs/KrakenSakura-Maelstrom-12B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EldritchLabs/KrakenSakura-Maelstrom-12B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="EldritchLabs/KrakenSakura-Maelstrom-12B-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EldritchLabs/KrakenSakura-Maelstrom-12B-v1")
model = AutoModelForCausalLM.from_pretrained("EldritchLabs/KrakenSakura-Maelstrom-12B-v1", 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]:]))

NeMo
How to use EldritchLabs/KrakenSakura-Maelstrom-12B-v1 with NeMo:
```
# tag did not correspond to a valid NeMo domain.
```
Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EldritchLabs/KrakenSakura-Maelstrom-12B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EldritchLabs/KrakenSakura-Maelstrom-12B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EldritchLabs/KrakenSakura-Maelstrom-12B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EldritchLabs/KrakenSakura-Maelstrom-12B-v1

SGLang

How to use EldritchLabs/KrakenSakura-Maelstrom-12B-v1 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 "EldritchLabs/KrakenSakura-Maelstrom-12B-v1" \
    --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": "EldritchLabs/KrakenSakura-Maelstrom-12B-v1",
		"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 "EldritchLabs/KrakenSakura-Maelstrom-12B-v1" \
        --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": "EldritchLabs/KrakenSakura-Maelstrom-12B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EldritchLabs/KrakenSakura-Maelstrom-12B-v1 with Docker Model Runner:
```
docker model run hf.co/EldritchLabs/KrakenSakura-Maelstrom-12B-v1
```

Checkpoints 19, 20, 24, 29, 36 and 40

by Naphula - opened Apr 4

Discussion

Naphula

EldritchLabs org Apr 4

I've archived some notable checkpoints here (the rest are being deleted):

https://huggingface.co/Naphula-Archives/S19-passthrough
https://huggingface.co/Naphula-Archives/S20-pdq
https://huggingface.co/Naphula-Archives/S24-qhe
https://huggingface.co/Naphula-Archives/S29-multislerp
https://huggingface.co/Naphula-Archives/S36-magic
https://huggingface.co/Naphula-Archives/S40-cvs

Of the above, Magic is probably my favorite, despite being partially censored.

S38-dare_linear is also good but easily recreated, so no need to upload it.

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