Instructions to use mjpsm/activity-generation-model-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mjpsm/activity-generation-model-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="mjpsm/activity-generation-model-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mjpsm/activity-generation-model-v0.1")
model = AutoModelForCausalLM.from_pretrained("mjpsm/activity-generation-model-v0.1", 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 mjpsm/activity-generation-model-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mjpsm/activity-generation-model-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mjpsm/activity-generation-model-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mjpsm/activity-generation-model-v0.1

SGLang

How to use mjpsm/activity-generation-model-v0.1 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 "mjpsm/activity-generation-model-v0.1" \
    --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": "mjpsm/activity-generation-model-v0.1",
		"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 "mjpsm/activity-generation-model-v0.1" \
        --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": "mjpsm/activity-generation-model-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mjpsm/activity-generation-model-v0.1 with Docker Model Runner:
```
docker model run hf.co/mjpsm/activity-generation-model-v0.1
```

activity-generation-model-v0.1 / README.md

mjpsm

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metadata

base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
pipeline_tag: text-generation
tags:
  - fine-tuned
  - activity-generation
  - education
  - structured-output
  - qwen

Activity Generation Model v0.1

This model generates one next educational activity from a student's knowledge submission.

Input

A student knowledge submission.

Output

The model is trained to return JSON with:

title
description
instructions
activityType
estimatedMinutes

Allowed activity types:

REFLECTION
RESEARCH
COLLABORATE
CREATE
PRACTICE
EXPERIENCE
TEACH

Training data

The proof-of-concept training dataset contained approximately 200 synthetic input-output examples derived from real knowledge-submission patterns.

Limitations

This is an early proof-of-concept model trained on a small and imbalanced dataset. It may favor common activity types such as CREATE or PRACTICE, produce inaccurate time estimates, or return malformed JSON.