Instructions to use Xen0pp/Smollm3_ml_planner_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xen0pp/Smollm3_ml_planner_v2 with Transformers:

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

pipe = pipeline("text-generation", model="Xen0pp/Smollm3_ml_planner_v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Xen0pp/Smollm3_ml_planner_v2")
model = AutoModelForCausalLM.from_pretrained("Xen0pp/Smollm3_ml_planner_v2")
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 Xen0pp/Smollm3_ml_planner_v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Xen0pp/Smollm3_ml_planner_v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xen0pp/Smollm3_ml_planner_v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Xen0pp/Smollm3_ml_planner_v2

SGLang

How to use Xen0pp/Smollm3_ml_planner_v2 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 "Xen0pp/Smollm3_ml_planner_v2" \
    --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": "Xen0pp/Smollm3_ml_planner_v2",
		"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 "Xen0pp/Smollm3_ml_planner_v2" \
        --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": "Xen0pp/Smollm3_ml_planner_v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Xen0pp/Smollm3_ml_planner_v2 with Docker Model Runner:
```
docker model run hf.co/Xen0pp/Smollm3_ml_planner_v2
```

SmolLM ML Project Planning Assistant V2

Improved version of SmolLM2-360M fine-tuned for ML project planning and guidance.

🆕 What's New in V2

This model builds upon Xen0pp/Smollm3_720prms with additional training on ML project planning scenarios:

New Capabilities

✅ Project Scoping: Breaking down ML projects into phases
✅ Timeline Estimation: Realistic project timelines and budgets
✅ Architecture Selection: Domain-specific model recommendations
✅ Data Strategy: Handling limited data scenarios
✅ Deployment Planning: Production deployment guidance

Training Data

V1: 6 examples (general ML concepts, research papers)
V2: +5 examples (ML project planning, timelines, strategies)
Total: 11 curated examples covering end-to-end project lifecycle

🚀 Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Xen0pp/Smollm3_ml_planner_v2",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Xen0pp/Smollm3_ml_planner_v2")

# Ask about project planning
messages = [
    {"role": "system", "content": "You are an expert ML project planning advisor."},
    {"role": "user", "content": "I want to build a customer churn prediction model. What are the first steps?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs.to(model.device), max_new_tokens=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

💡 Example Queries

Project Planning:

"How should I plan a computer vision project for manufacturing quality control?"
"What's a realistic timeline for an NLP sentiment analysis project?"
"I want to build a recommendation system. Where do I start?"

Limited Data Scenarios:

"I only have 100 labeled examples. Can I still build a model?"
"What's the best approach with small datasets?"

Architecture Decisions:

"CNN vs Vision Transformer for my use case?"
"Which recommendation algorithm should I choose?"

📊 Model Details

Base: SmolLM2-360M-Instruct
V1 Training: 6 examples (general ML)
V2 Training: Continued from V1 + 5 project planning examples
Total Expertise: 11 examples
Parameters: 360M
Context Length: 2048 tokens
Training Method: Unsloth (2x faster fine-tuning)

🎯 Best For

ML practitioners planning new projects
Students learning project management
Teams scoping ML initiatives
Anyone needing structured ML guidance

⚠️ Limitations

Small training dataset (for educational purposes)
Should verify critical decisions with domain experts
Timelines and budgets are estimates
Best combined with real-world experience

📈 Comparison

Feature	V1	V2
General ML Concepts	✅	✅
Research Papers	✅	✅
Project Planning	❌	✅
Timeline Estimation	❌	✅
Budget Guidance	❌	✅
Domain-Specific Advice	Limited	✅

📝 License

Apache 2.0

🔗 Links

V1 Model: Xen0pp/Smollm3_720prms
Base Model: HuggingFaceTB/SmolLM2-360M-Instruct

Note: This is a fine-tuned small model for educational purposes. Always validate recommendations with domain expertise.

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Safetensors

Model size

0.4B params

Tensor type

BF16

Model tree for Xen0pp/Smollm3_ml_planner_v2

Base model

HuggingFaceTB/SmolLM2-360M

Quantized

HuggingFaceTB/SmolLM2-360M-Instruct

Finetuned

Xen0pp/Smollm3_720prms

Finetuned

(2)

this model