Instructions to use CastIronMind/Lissa-1.5B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CastIronMind/Lissa-1.5B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="CastIronMind/Lissa-1.5B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("CastIronMind/Lissa-1.5B-Instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CastIronMind/Lissa-1.5B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CastIronMind/Lissa-1.5B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CastIronMind/Lissa-1.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CastIronMind/Lissa-1.5B-Instruct

SGLang

How to use CastIronMind/Lissa-1.5B-Instruct 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 "CastIronMind/Lissa-1.5B-Instruct" \
    --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": "CastIronMind/Lissa-1.5B-Instruct",
		"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 "CastIronMind/Lissa-1.5B-Instruct" \
        --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": "CastIronMind/Lissa-1.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CastIronMind/Lissa-1.5B-Instruct with Docker Model Runner:
```
docker model run hf.co/CastIronMind/Lissa-1.5B-Instruct
```

CastIronMind commited on 3 days ago

Commit

dfebef7

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Model card: link the collection

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@@ -409,6 +409,8 @@ Hugging Face: [CastIronMind](https://huggingface.co/CastIronMind)
 Logo by [GDMNCat](https://twitter.com/GDMNCat).
 ## 17. Acknowledgements
 - **[gpugo.ru](https://gpugo.ru)** — without them I physically could not have trained this monster.

 Logo by [GDMNCat](https://twitter.com/GDMNCat).
+**Collection.** [Lissa 1.5B](https://huggingface.co/collections/CastIronMind/lissa-15b-6ada7e9d8e8d07dfba53b890) — base checkpoint, instruct checkpoint and the supervised fine-tuning datasets in one place.
 ## 17. Acknowledgements
 - **[gpugo.ru](https://gpugo.ru)** — without them I physically could not have trained this monster.