Instructions to use kroffske/shellcue-lfm2.5-230m-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kroffske/shellcue-lfm2.5-230m-alpha with Transformers:

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

pipe = pipeline("text-generation", model="kroffske/shellcue-lfm2.5-230m-alpha")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kroffske/shellcue-lfm2.5-230m-alpha")
model = AutoModelForCausalLM.from_pretrained("kroffske/shellcue-lfm2.5-230m-alpha", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kroffske/shellcue-lfm2.5-230m-alpha with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kroffske/shellcue-lfm2.5-230m-alpha"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kroffske/shellcue-lfm2.5-230m-alpha",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kroffske/shellcue-lfm2.5-230m-alpha

SGLang

How to use kroffske/shellcue-lfm2.5-230m-alpha 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 "kroffske/shellcue-lfm2.5-230m-alpha" \
    --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": "kroffske/shellcue-lfm2.5-230m-alpha",
		"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 "kroffske/shellcue-lfm2.5-230m-alpha" \
        --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": "kroffske/shellcue-lfm2.5-230m-alpha",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kroffske/shellcue-lfm2.5-230m-alpha with Docker Model Runner:
```
docker model run hf.co/kroffske/shellcue-lfm2.5-230m-alpha
```

shellcue-lfm2.5-230m-alpha / schema.json

kroffske

Publish ShellCue LFM2.5 230M development alpha

ae5b485 verified 22 days ago

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222 Bytes

	{
	"model_fields": [
	"context_text",
	"typed_prefix_masked"
	],
	"schema_version": "shellcue.input.v1",
	"target_fields": [
	"target_next_piece",
	"target_suffix_masked",
	"target_command_masked"
	]
	}