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 / model.json

kroffske

Publish ShellCue LFM2.5 230M development alpha

ae5b485 verified 23 days ago

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842 Bytes

	{
	"artifact_kind": "neural_causal_lm",
	"file_hashes": {
	"config.json": "df8f3ce731cd92b8a39c101ddb5a6c701a8c12ff1bf8c48f5c87d8c64e4e2387",
	"generation_config.json": "e6a1ce8f16f9c3d6ac1f13bee18484f271a2566b5af238853c90fb9b2b5c5cda",
	"model.safetensors": "c4f7973c48eb04fa2e8013f0d03171fcfb4ee27c157dea31e96020b12b84fb53",
	"tokenizer.json": "df1d8d5ec5d091b460562ffd545e4a5e91d17d4a0db7ebe733be34ed374377bd",
	"tokenizer_config.json": "75c287923e252b08b0a0f1c367bbe557ab23a681d0b71c5a34e0932ddbe2f5ee"
	},
	"input_fields": [
	"context_text",
	"typed_prefix_masked"
	],
	"runtime_min_version": "0.1.0a1",
	"schema_version": "shellcue.model.v1",
	"tokenizer_config_path": "tokenizer_config.json",
	"tokenizer_path": "tokenizer.json",
	"weights_format": "safetensors",
	"weights_path": "model.safetensors"
	}