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

Libraries

How to use CastIronMind/LissaNano-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="CastIronMind/LissaNano-Instruct")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CastIronMind/LissaNano-Instruct")
model = AutoModelForCausalLM.from_pretrained("CastIronMind/LissaNano-Instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CastIronMind/LissaNano-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CastIronMind/LissaNano-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CastIronMind/LissaNano-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CastIronMind/LissaNano-Instruct

SGLang

How to use CastIronMind/LissaNano-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/LissaNano-Instruct" \
    --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": "CastIronMind/LissaNano-Instruct",
		"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 "CastIronMind/LissaNano-Instruct" \
        --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": "CastIronMind/LissaNano-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

CastIronMind commited on 2 days ago

Commit

5632649

verified ·

1 Parent(s): 2efc89e

link GGUF quantizations (LissaNano-Instruct-GGUF)

Browse files

Files changed (1) hide show

README.md +2 -0

README.md CHANGED Viewed

@@ -88,6 +88,8 @@ model_card_authors:
 | [Lissa 1.5B Instruct](https://huggingface.co/CastIronMind/Lissa-1.5B-Instruct) | 1.45B | 2.9 GB | a full roleplay partner |
 | [Lissa 1.5B Base](https://huggingface.co/CastIronMind/Lissa-1.5B-Base) | 1.45B | 2.9 GB | pretraining, research |
 ## 4. Requirements
 ```

 | [Lissa 1.5B Instruct](https://huggingface.co/CastIronMind/Lissa-1.5B-Instruct) | 1.45B | 2.9 GB | a full roleplay partner |
 | [Lissa 1.5B Base](https://huggingface.co/CastIronMind/Lissa-1.5B-Base) | 1.45B | 2.9 GB | pretraining, research |
+**GGUF quantizations:** [LissaNano-Instruct-GGUF](https://huggingface.co/CastIronMind/LissaNano-Instruct-GGUF) — 15 files from Q8_0 down to IQ2, each with measured perplexity against this bf16 checkpoint.
 ## 4. Requirements
 ```