templates/model_card.md

{{model_name}}

{{model_description}}

Model Details

• Base Model: SmolLM3-3B
• Model Type: Causal Language Model
• Languages: English, French
• License: Apache 2.0
• Fine-tuned: Yes {{#if quantized_models}}
• Quantized Versions: Available in subdirectories {{/if}}

Usage

Main Model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the main model
model = AutoModelForCausalLM.from_pretrained(
    "{{repo_name}}",
    device_map="auto",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained("{{repo_name}}")

# Generate text
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to(model.device.type)
output = model.generate(**input_ids, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Training Information

Training Configuration

• Base Model: {{base_model}}
• Dataset: {{dataset_name}}
• Training Config: {{training_config_type}}
• Trainer Type: {{trainer_type}} {{#if dataset_sample_size}}
• Dataset Sample Size: {{dataset_sample_size}} {{/if}}

Training Parameters

• Batch Size: {{batch_size}}
• Gradient Accumulation: {{gradient_accumulation_steps}}
• Learning Rate: {{learning_rate}}
• Max Epochs: {{max_epochs}}
• Sequence Length: {{max_seq_length}}

Training Infrastructure

• Hardware: {{hardware_info}}
• Monitoring: Trackio integration
• Experiment: {{experiment_name}}

Model Architecture

This is a fine-tuned version of the SmolLM3-3B model with the following specifications:

• Base Model: SmolLM3-3B
• Parameters: ~3B
• Context Length: {{max_seq_length}}
• Languages: English, French
• Architecture: Transformer-based causal language model

Performance

The model provides:

• Text Generation: High-quality text generation capabilities
• Conversation: Natural conversation abilities
• Multilingual: Support for English and French {{#if quantized_models}}
• Quantized Versions: Optimized for different deployment scenarios {{/if}}

Limitations

1. Context Length: Limited by the model's maximum sequence length
2. Bias: May inherit biases from the training data
3. Factual Accuracy: May generate incorrect or outdated information
4. Safety: Should be used responsibly with appropriate safeguards {{#if quantized_models}}
5. Quantization: Quantized versions may have slightly reduced accuracy {{/if}}

Training Data

The model was fine-tuned on:

• Dataset: {{dataset_name}}
• Size: {{dataset_size}}
• Format: {{dataset_format}}
• Languages: English, French

Evaluation

The model was evaluated using:

• Metrics: Loss, perplexity, and qualitative assessment
• Monitoring: Real-time tracking via Trackio
• Validation: Regular validation during training

Citation

If you use this model in your research, please cite:

@misc{{{model_name_slug}},
  title={{{{model_name}}}},
  author={{{author_name}}},
  year={2024},
  url={https://huggingface.co/{{repo_name}}}
}

License

This model is licensed under the Apache 2.0 License.

Acknowledgments

• Base Model: SmolLM3-3B by HuggingFaceTB
• Training Framework: PyTorch, Transformers, PEFT
• Monitoring: Trackio integration
• Quantization: torchao library

Support

For questions and support:

• Open an issue on the Hugging Face repository
• Check the model documentation
• Review the training logs and configuration

Repository Structure

{{repo_name}}/
├── README.md (this file)
├── config.json
├── pytorch_model.bin
├── tokenizer.json
└── tokenizer_config.json

Usage Examples

Text Generation

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("{{repo_name}}")
tokenizer = AutoTokenizer.from_pretrained("{{repo_name}}")

text = "The future of artificial intelligence is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Conversation

def chat_with_model(prompt, max_length=100):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=max_length)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

response = chat_with_model("Hello, how are you today?")
print(response)

Advanced Usage

# With generation parameters
outputs = model.generate(
    **inputs,
    max_new_tokens=100,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

Monitoring and Tracking

This model was trained with comprehensive monitoring:

• Trackio Space: {{trackio_url}}
• Experiment: {{experiment_name}}
• Dataset Repository: https://huggingface.co/datasets/{{dataset_repo}}
• Training Logs: Available in the experiment data

Deployment

Requirements

pip install torch transformers accelerate
{{#if quantized_models}}
pip install torchao  # For quantized models
{{/if}}

Hardware Requirements

• Main Model: GPU with 8GB+ VRAM recommended

Changelog

• v1.0.0: Initial release with fine-tuned model