Instructions to use micrictor/LFM2.5-350M-ShellAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use micrictor/LFM2.5-350M-ShellAI with Transformers:

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

pipe = pipeline("text-generation", model="micrictor/LFM2.5-350M-ShellAI")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("micrictor/LFM2.5-350M-ShellAI")
model = AutoModelForCausalLM.from_pretrained("micrictor/LFM2.5-350M-ShellAI", device_map="auto")
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 Settings

vLLM

How to use micrictor/LFM2.5-350M-ShellAI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "micrictor/LFM2.5-350M-ShellAI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "micrictor/LFM2.5-350M-ShellAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/micrictor/LFM2.5-350M-ShellAI

SGLang

How to use micrictor/LFM2.5-350M-ShellAI 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 "micrictor/LFM2.5-350M-ShellAI" \
    --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": "micrictor/LFM2.5-350M-ShellAI",
		"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 "micrictor/LFM2.5-350M-ShellAI" \
        --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": "micrictor/LFM2.5-350M-ShellAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use micrictor/LFM2.5-350M-ShellAI with Docker Model Runner:
```
docker model run hf.co/micrictor/LFM2.5-350M-ShellAI
```

LFM2.5-350M-ShellAI

ShellAI Bash-command model distilled at the response level from LiquidAI/LFM2.5-2.6B into LiquidAI/LFM2.5-350M.

This repository contains the merged BF16 training checkpoint.

The model is trained to emit exactly one command inside:

<shellai-command>command</shellai-command>

General chat anchors, assistant-only loss, LoRA, one training epoch, early stopping, and a pre-publication retention gate are used to reduce catastrophic forgetting. No generated command was executed during dataset construction or evaluation.

Distillation

The two models have different vocabularies (128K teacher versus 65,536 student), so this uses sequence-level response distillation rather than token-logit KL. Teacher candidates that fail the command envelope, primary-utility, or reference- similarity gates fall back to the verified dataset reference.

Evaluation

Held-out repository test split (300 examples, BF16 Transformers):

Model	Exact	Utility match	Token F1	Valid envelope
Base 350M	5.7%	23.0%	0.225	100.0%
Distilled 350M	4.0%	39.7%	0.305	100.0%

Chat retention used 50 non-shell prompts: shell-envelope leakage remained 0.0%; anchor similarity retained 84.9% of baseline.

Q8_0 llama.cpp CPU test (same 50-example subset):

Threads	Token F1	Utility match	Median latency	Decode
1	0.268	36.0%	1278 ms	16.6 tok/s
2	0.268	36.0%	641 ms	32.5 tok/s

License

This is a modified derivative of Liquid AI's LFM2.5 weights and is distributed under the included LFM Open License v1.0. See NOTICE for modification details.

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Safetensors

Model size

0.4B params

Tensor type

BF16

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