Instructions to use turnercore/minicpm5-1b-automaticity-v9-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use turnercore/minicpm5-1b-automaticity-v9-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-1B")
model = PeftModel.from_pretrained(base_model, "turnercore/minicpm5-1b-automaticity-v9-lora")

Transformers

How to use turnercore/minicpm5-1b-automaticity-v9-lora with Transformers:

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

pipe = pipeline("text-generation", model="turnercore/minicpm5-1b-automaticity-v9-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("turnercore/minicpm5-1b-automaticity-v9-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use turnercore/minicpm5-1b-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/minicpm5-1b-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/minicpm5-1b-automaticity-v9-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/turnercore/minicpm5-1b-automaticity-v9-lora

SGLang

How to use turnercore/minicpm5-1b-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/minicpm5-1b-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/minicpm5-1b-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/minicpm5-1b-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/minicpm5-1b-automaticity-v9-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use turnercore/minicpm5-1b-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/minicpm5-1b-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/minicpm5-1b-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/minicpm5-1b-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/minicpm5-1b-automaticity-v9-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use turnercore/minicpm5-1b-automaticity-v9-lora with Docker Model Runner:
```
docker model run hf.co/turnercore/minicpm5-1b-automaticity-v9-lora
```

MiniCPM5 1B + Automaticity V9 LoRA

Rank-16 response-only LoRA trained for one epoch on the private Automaticity V9 friendly direct-tool corpus. This is a strong 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 MiniCPM5's native XML tool calls with thinking disabled and loss only on the assistant turn.

Training

Base: openbmb/MiniCPM5-1B
Base/tokenizer revision: 4e9de7a0778dc1c362e983e6858f0e77542cbdca
Rows: 4,900; dataset SHA-256: f3421604542d8f333576db814b471751900bc6aa2cfe109084c68d0f9ddf9c20
Context: 2,048 tokens; no truncation; maximum rendered row 2,036 tokens
Precision: BF16 LoRA, not QLoRA
LoRA: rank 16, alpha 32, dropout 0.05; attention and MLP projections
Epochs: 1; cosine learning-rate schedule; 3% warmup
Peak learning rate: 2e-4
Effective batch: 16 (1 x 16 gradient accumulation)
Seed: 3407
Loss: native assistant response only
Trainer runtime: 1,020.55 seconds
Adapter SHA-256: 8c0f24b5fce0063237b6f89ea22558b013b9e454bb3009895b0f81c1f8a65209

Frozen validation result

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

Metric	Result
End-to-end exact	90.86%
Routing	98.38%
Action exact	69.55%
No-tool precision	100%
No-tool recall	99.86%
Listed-tool rate	100%
Valid-call rate	100%
Latency average	0.272 s
Latency p50	0.141 s
Latency p95	0.910 s
No-tool latency average / p95	0.130 s / 0.177 s
Action latency average / p95	0.607 s / 1.671 s

The untuned base on the identical CUDA validation condition scored 16.29% end-to-end exact, 28.38% routing, 52.24% action exact, and 1.08% no-tool recall.

Limitations

This adapter is not yet promoted for autonomous execution. The frozen validation set still contains 17 wrong-tool rows and 79 wrong-argument rows; action exact is 69.55%. Nested XML arguments are a recurring failure mode. 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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