Instructions to use Alignment-Lab-AI/Scrollplay with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alignment-Lab-AI/Scrollplay with Transformers:

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

pipe = pipeline("text-generation", model="Alignment-Lab-AI/Scrollplay")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Alignment-Lab-AI/Scrollplay")
model = AutoModelForCausalLM.from_pretrained("Alignment-Lab-AI/Scrollplay")
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

vLLM

How to use Alignment-Lab-AI/Scrollplay with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Alignment-Lab-AI/Scrollplay

SGLang

How to use Alignment-Lab-AI/Scrollplay 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 "Alignment-Lab-AI/Scrollplay" \
    --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": "Alignment-Lab-AI/Scrollplay",
		"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 "Alignment-Lab-AI/Scrollplay" \
        --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": "Alignment-Lab-AI/Scrollplay",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Alignment-Lab-AI/Scrollplay with Docker Model Runner:
```
docker model run hf.co/Alignment-Lab-AI/Scrollplay
```

Scrollplay

File size: 1,136 Bytes

3083b00

---
base_model:
- tavtav/eros-7b-test
- Nexusflow/Starling-LM-7B-beta
library_name: transformers
tags:
- mergekit
- merge

---
# output

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the breadcrumbs_ties merge method using [tavtav/eros-7b-test](https://huggingface.co/tavtav/eros-7b-test) as a base.

### Models Merged

The following models were included in the merge:
* [Nexusflow/Starling-LM-7B-beta](https://huggingface.co/Nexusflow/Starling-LM-7B-beta)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
slices:
- sources:
  - model: tavtav/eros-7b-test
    layer_range: [0, 32]
  - model: Nexusflow/Starling-LM-7B-beta
    layer_range: [0, 32]
    parameters:
      weight: [1, 0.686, 0.37185, 0.686, 1]
      density: [0.9, 0.7, 0.9] # density gradient
      gamma: [0.01, 0.03, 0.02, 0.01] # weight gradient
tokenizer_source: base
merge_method: breadcrumbs_ties
base_model: tavtav/eros-7b-test
parameters:
  normalize: true
dtype: bfloat16
name: scringle

```