Instructions to use SpaceYL/ECE_Poirot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SpaceYL/ECE_Poirot with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SpaceYL/ECE_Poirot")
model = AutoModelForCausalLM.from_pretrained("SpaceYL/ECE_Poirot", 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 SpaceYL/ECE_Poirot with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SpaceYL/ECE_Poirot

SGLang

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

Docker Model Runner
How to use SpaceYL/ECE_Poirot with Docker Model Runner:
```
docker model run hf.co/SpaceYL/ECE_Poirot
```

ECE_Poirot / README.md

SpaceYL

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	---
	base_model:
	- bond005/meno-tiny-0.1
	- Qwen/Qwen2.5-1.5B-Instruct
	library_name: transformers
	tags:
	- mergekit
	- merge
	license: apache-2.0
	---
	# SpaceYL/ECE_Poirot

	First model merged on the ECE intelligence Lab proprietary GPUs

	This model has been produced by:
	- LALAIN Youri, engineering student at French Engineering School ECE
	- RAGE LILIAN, engineering student at French Engineering School ECE

	Under the supervision of:
	- Andre-Louis Rochet, Lecturer at ECE, Co-founder at TW3 Partners
	- Paul Lemaistre, Lecturer at ECE, CTO at TW3 Partners
	- Mohammed Mounir, Solution Architect at Exaion
	- Hervé Chibois, Infrastructure Expert at Exaion
	- Des Bontés Sonafouo, Chef de projet IT at Omnes

	With the contribution of:
	- ECE engineering school as sponsor and financial contributor
	- François STEPHAN as director of ECE
	- Gérard REUS as acting director of iLAB


	### Supervisory structure
	The iLab (intelligence Lab) is a structure created by the ECE and dedicated to artificial intelligence

	### About ECE
	ECE, a multi-program, multi-campus, and multi-sector engineering school specializing in digital engineering, trains engineers and technology experts for the 21st century, capable of meeting the challenges of the dual digital and sustainable development revolutions.



	### Models Merged

	The following models were included in the merge:
	* [bond005/meno-tiny-0.1](https://huggingface.co/bond005/meno-tiny-0.1)
	* [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	slices:
	- sources:
	- model: bond005/meno-tiny-0.1
	layer_range: [0, 28]
	- model: Qwen/Qwen2.5-1.5B-Instruct
	layer_range: [0, 28]
	merge_method: slerp
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	parameters:
	t:
	- filter: self_attn
	value: [0, 0.25, 0.5, 0.75, 1]
	- filter: mlp
	value: [1, 0.75, 0.5, 0.25, 0]
	- value: 0.5
	dtype: bfloat16

	```