Instructions to use mzen/EventModel-1.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mzen/EventModel-1.2B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("liquidai/LFM2-1.2B")
model = PeftModel.from_pretrained(base_model, "mzen/EventModel-1.2B")

Transformers

How to use mzen/EventModel-1.2B with Transformers:

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

pipe = pipeline("text-generation", model="mzen/EventModel-1.2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mzen/EventModel-1.2B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mzen/EventModel-1.2B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mzen/EventModel-1.2B

SGLang

How to use mzen/EventModel-1.2B 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 "mzen/EventModel-1.2B" \
    --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": "mzen/EventModel-1.2B",
		"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 "mzen/EventModel-1.2B" \
        --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": "mzen/EventModel-1.2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mzen/EventModel-1.2B with Docker Model Runner:
```
docker model run hf.co/mzen/EventModel-1.2B
```

EventModel-1.2B / README.md

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	---
	base_model: liquidai/LFM2-1.2B
	library_name: peft
	model_name: EventModel-1.2B
	tags:
	- base_model:adapter:liquidai/LFM2-1.2B
	- lora
	- sft
	- transformers
	- trl
	licence: license
	pipeline_tag: text-generation
	---

	# Model Card for EventModel-1.2B

	EventModel is a 1.2B parameter model finetune of LFM2-1.2B using data extracted from r/parents. The idea is to come up with problems that a kid of certain age would face. This is done by using data from r/Parenting, analyzing the problem, analyzing the kid group using iterative few-shot prompting, then finetunning a generative model with the results.

	It has been trained using [TRL](https://github.com/huggingface/trl).

	## Quick start

	```python
	from transformers import pipeline

	pipe = pipeline(
	"text-generation",
	model="mzen/EventModel-1.2B",
	trust_remote_code=True,
	device_map="auto"
	)

	prompt = "### Character: 13 year old, boy\n\n### Problem:"

	output = pipe(
	prompt,
	max_new_tokens=512,
	do_sample=True,
	temperature=0.7,
	top_p=0.9,
	return_full_text=False
	)

	print(output[0]['generated_text'])
	```

	## Training procedure




	This model was trained with SFT.

	### Framework versions

	- PEFT 0.18.1
	- TRL: 0.27.2
	- Transformers: 5.1.0
	- Pytorch: 2.10.0
	- Datasets: 4.5.0
	- Tokenizers: 0.22.2

	## Citations



	Cite TRL as:

	```bibtex
	@misc{vonwerra2022trl,
	title = {{TRL: Transformer Reinforcement Learning}},
	author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year = 2020,
	journal = {GitHub repository},
	publisher = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
	}
	```