Instructions to use Mincofficial/Minico-M1-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mincofficial/Minico-M1-Preview with Transformers:

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

pipe = pipeline("text-generation", model="Mincofficial/Minico-M1-Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mincofficial/Minico-M1-Preview")
model = AutoModelForCausalLM.from_pretrained("Mincofficial/Minico-M1-Preview", 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 Mincofficial/Minico-M1-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mincofficial/Minico-M1-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mincofficial/Minico-M1-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mincofficial/Minico-M1-Preview

SGLang

How to use Mincofficial/Minico-M1-Preview 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 "Mincofficial/Minico-M1-Preview" \
    --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": "Mincofficial/Minico-M1-Preview",
		"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 "Mincofficial/Minico-M1-Preview" \
        --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": "Mincofficial/Minico-M1-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Mincofficial/Minico-M1-Preview with Docker Model Runner:
```
docker model run hf.co/Mincofficial/Minico-M1-Preview
```

Minico-M1

Preview release of Minico-M1, fine-tuned from LiquidAI/LFM2.5-350M on QyrouNnet-AI/exp-reasoning-effort-control. The tokenizer chat template supports visible <think>...</think> reasoning blocks and the low, medium, high, and max effort presets.

Important checkpoint note

The only retained local model artifact was Minico-M1.gguf in Q8_0 format. model.safetensors here is a float16 dequantized export of that Q8_0 file, not the original full-precision training checkpoint. Its weights are therefore approximate to the Q8 model and are intended for interoperability and preview use.

Source GGUF SHA-256: 5259c3090e39d2f1fef545c78b254fa60851b8bf678e9f8e92878ff79322ec99

Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Mincofficial/Minico-M1-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto")
messages = [{"role": "user", "content": "Explain why the sky is blue."}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    enable_thinking=True,
    reasoning_effort="medium",
    return_tensors="pt",
)
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))

The upstream LiquidAI/LFM2.5-350M license applies.

Downloads last month: 469

Safetensors

Model size

0.4B params

Tensor type

F16

Model tree for Mincofficial/Minico-M1-Preview

Base model

LiquidAI/LFM2.5-350M-Base

Finetuned

LiquidAI/LFM2.5-350M

Finetuned

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