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)

# pip install -U transformers accelerate
# 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=256)
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

File size: 1,878 Bytes

4666b42

---

license: other
license_name: lfm1.0
base_model: LiquidAI/LFM2.5-350M
tags:
- lfm2
- distillation
- bash
- shellai
- transformers
---


# 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:

```text

<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.