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 / README.md

turnercore

Publish LFM2.5 Automaticity V9 response-only LoRA

0cbacae verified 17 days ago

preview code

Raw

History Blame Contribute Delete

2.91 kB

	---
	license: other
	base_model: LiquidAI/LFM2.5-1.2B-Instruct
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- function-calling
	- tool-use
	- automaticity
	- automaticity-v9
	- lora
	- sft
	- transformers
	- trl
	- unsloth
	---

	# 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`.