Instructions to use turnercore/lfm2.5-1.2b-automaticity-v9-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use turnercore/lfm2.5-1.2b-automaticity-v9-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-1.2B-Instruct")
model = PeftModel.from_pretrained(base_model, "turnercore/lfm2.5-1.2b-automaticity-v9-lora")

Transformers

How to use turnercore/lfm2.5-1.2b-automaticity-v9-lora with Transformers:

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

pipe = pipeline("text-generation", model="turnercore/lfm2.5-1.2b-automaticity-v9-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("turnercore/lfm2.5-1.2b-automaticity-v9-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use turnercore/lfm2.5-1.2b-automaticity-v9-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "turnercore/lfm2.5-1.2b-automaticity-v9-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "turnercore/lfm2.5-1.2b-automaticity-v9-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/turnercore/lfm2.5-1.2b-automaticity-v9-lora

SGLang

How to use turnercore/lfm2.5-1.2b-automaticity-v9-lora 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 "turnercore/lfm2.5-1.2b-automaticity-v9-lora" \
    --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": "turnercore/lfm2.5-1.2b-automaticity-v9-lora",
		"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 "turnercore/lfm2.5-1.2b-automaticity-v9-lora" \
        --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": "turnercore/lfm2.5-1.2b-automaticity-v9-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use turnercore/lfm2.5-1.2b-automaticity-v9-lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for turnercore/lfm2.5-1.2b-automaticity-v9-lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for turnercore/lfm2.5-1.2b-automaticity-v9-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for turnercore/lfm2.5-1.2b-automaticity-v9-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="turnercore/lfm2.5-1.2b-automaticity-v9-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use turnercore/lfm2.5-1.2b-automaticity-v9-lora with Docker Model Runner:
```
docker model run hf.co/turnercore/lfm2.5-1.2b-automaticity-v9-lora
```

LFM2.5 1.2B + Automaticity V9 LoRA

Rank-16 response-only LoRA trained for one epoch on the private Automaticity V9 friendly direct-tool corpus. This is the strongest current V9 validation candidate, not a production-promoted autonomous router.

The model routes one current thought to at most one available tool, or makes no tool call. Training used LFM2.5's native marked Python-call-list format and loss only on the assistant turn.

Training

Base: LiquidAI/LFM2.5-1.2B-Instruct
Base/tokenizer revision: 868df74dd56ff8a0c2ac5dbf281690c2dbebe4c9
Rows: 4,900; dataset SHA-256: 3fb79e5fe3cf762b3258c5674a806903e310aebb35d8ed153a525b0377b3bd8f
Context: 2,048 tokens; no truncation; maximum rendered row 1,978 tokens
Precision: ROCm BF16 LoRA, not QLoRA
LoRA: rank 16, alpha 16, dropout 0; q/k/v/out/in_proj and w1/w2/w3
Epochs: 1; linear learning-rate schedule; 3% warmup
Peak learning rate: 2e-4; weight decay: 0.001
Effective batch: 16 (4 x 4 gradient accumulation)
Seed: 3407
Loss: native assistant response only
Trainer runtime: 4,037 seconds
Adapter SHA-256: e81bdda7e1a684ae3a3f8d952303446d974c9ededf0cf383b8c76791112340ea

Frozen validation result

Evaluation used 1,050 private validation rows with normal five-tool retrieval, no gold injection, 100% action-gold retrieval recall, and no decoding constraint. The validation dataset SHA-256 is 85094c96ca7fa2f96cbb0f7f85bd08510d56b9d4639646156d8806680bca9715.

Metric	Result
End-to-end exact	95.33%
Routing	98.00%
Action exact	84.94%
No-tool precision	99.86%
No-tool recall	99.73%
Argument schema validity	99.90%
Listed-tool rate	99.90%
Valid-call rate	100%
Latency average	1.242 s
Latency p50	0.518 s
Latency p95	4.520 s
No-tool latency average / p95	0.471 s / 0.661 s
Action latency average / p95	3.065 s / 8.413 s

The untuned base on the identical ROCm validation condition scored 32.29% end-to-end exact, 45.52% routing, 29.17% action exact, and 33.60% no-tool recall.

Limitations

This adapter is not yet promoted for autonomous execution. The frozen validation set still contains 20 wrong-tool rows, 28 wrong-argument rows, and one unlisted call. Action p95 latency is 8.413 seconds, 6.3% slower than the untuned action p95 even though aggregate latency improved substantially. Use strict listed-name and schema validation or constrained decoding and reject invalid calls at runtime. Constraints cannot repair semantically wrong listed tools or schema-valid wrong arguments.

The private dataset and row-level evaluation repository is turnercore/automaticity-v9.

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Model tree for turnercore/lfm2.5-1.2b-automaticity-v9-lora

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Adapter

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