Instructions to use Xen0pp/SmolLM-ML-Planner-500-V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xen0pp/SmolLM-ML-Planner-500-V3 with Transformers:

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

pipe = pipeline("text-generation", model="Xen0pp/SmolLM-ML-Planner-500-V3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Xen0pp/SmolLM-ML-Planner-500-V3")
model = AutoModelForCausalLM.from_pretrained("Xen0pp/SmolLM-ML-Planner-500-V3")
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

vLLM

How to use Xen0pp/SmolLM-ML-Planner-500-V3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Xen0pp/SmolLM-ML-Planner-500-V3"
# 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/SmolLM-ML-Planner-500-V3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Xen0pp/SmolLM-ML-Planner-500-V3

SGLang

How to use Xen0pp/SmolLM-ML-Planner-500-V3 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/SmolLM-ML-Planner-500-V3" \
    --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/SmolLM-ML-Planner-500-V3",
		"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/SmolLM-ML-Planner-500-V3" \
        --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/SmolLM-ML-Planner-500-V3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Xen0pp/SmolLM-ML-Planner-500-V3 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Xen0pp/SmolLM-ML-Planner-500-V3 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Xen0pp/SmolLM-ML-Planner-500-V3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Xen0pp/SmolLM-ML-Planner-500-V3 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Xen0pp/SmolLM-ML-Planner-500-V3",
    max_seq_length=2048,
)

Docker Model Runner
How to use Xen0pp/SmolLM-ML-Planner-500-V3 with Docker Model Runner:
```
docker model run hf.co/Xen0pp/SmolLM-ML-Planner-500-V3
```

🤖 SmolLM ML Project Planner V3 (500 Examples)

Production-grade ML project planning assistant - Your expert advisor for designing, scoping, and executing machine learning projects.

🌟 What's New in V3

Major Upgrade: Trained on 500 comprehensive ML project planning examples

Training Results (Excellent!)

Final Training Loss: 0.0516 ⭐ (extremely low!)
Validation Loss: 0.0516
Training Examples: 450
Validation Examples: 50
Epochs: 5
Training Time: ~6 minutes (Unsloth)
GPU Memory: 1.076 GB peak

Capabilities

✅ Expert-level project initiation & scoping ✅ Comprehensive data strategy planning
✅ Domain-specific model selection (CV, NLP, RecSys, TS) ✅ Detailed & realistic timeline estimation ✅ Complete budget breakdowns ✅ Production-ready risk assessment ✅ Full MLOps & deployment guidance

🚀 Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model
model = AutoModelForCausalLM.from_pretrained(
    "Xen0pp/SmolLM-ML-Planner-500-V3",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Xen0pp/SmolLM-ML-Planner-500-V3")

# Ask for ML project planning advice
messages = [
    {"role": "system", "content": "You are an expert ML project planning advisor."},
    {"role": "user", "content": "How do I plan a recommendation system project?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=400, temperature=0.7, top_p=0.9)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

💡 Example Outputs

Input: "I have 300 labeled examples. Can I build a production model?"

V3 Output: Complete strategy including transfer learning, data augmentation, active learning, expected accuracy ranges, and cost-benefit analysis.

Input: "What's a realistic timeline for NLP sentiment analysis?"

V3 Output: Phase-by-phase breakdown: PoC (3-4 weeks, $10K-$25K), MVP (8-12 weeks, $50K-$100K), Production (5-7 months, $150K-$300K).

📊 Model Details

Base: SmolLM2-360M-Instruct
Parameters: 370M total, 8.7M trainable (2.34%)
Context: 2048 tokens
Size: 720MB (BF16)
Training: Unsloth LoRA (r=16, alpha=16)

🆚 Version History

Version	Examples	Loss	Use Case
V1	6	-	Demo
V3	500	0.0516	Production ⭐

📝 License

Apache 2.0

🔗 Links

Base Model: HuggingFaceTB/SmolLM2-360M-Instruct
Training: Unsloth

Built with ❤️ for the ML community - Helping practitioners plan successful ML projects

Downloads last month: 33

Safetensors

Model size

0.4B params

Tensor type

BF16

Model tree for Xen0pp/SmolLM-ML-Planner-500-V3

Base model

HuggingFaceTB/SmolLM2-360M

Quantized

HuggingFaceTB/SmolLM2-360M-Instruct

Finetuned

Xen0pp/Smollm3_720prms

Finetuned

(2)

this model