Model Card for MLC Model

Model Details

Model Description

The MLC Model is a conversational language model fine-tuned from the togethercomputer/RedPajama-INCITE-Chat-3B-v1 base model. It is designed to generate human-like text responses in English, suitable for applications such as chatbots and interactive question-answering systems. The model has been optimized using the MLC-LLM framework, which employs advanced quantization and TVM-based compilation techniques to enhance inference performance without compromising response quality.

Developed by: Ekincan Casim
Model type: Conversational Language Model
Language(s): English
License: MIT
Finetuned from model: togethercomputer/RedPajama-INCITE-Chat-3B-v1

Model Sources

Repository: https://huggingface.co/eccsm/mlc_llm
Demo: https://ekincan.casim.net

Uses

Direct Use

The MLC Model is intended for direct use in conversational AI applications, including:

Chatbots: Providing real-time, contextually relevant responses in customer service or virtual assistant scenarios.
Interactive Q&A Systems: Answering user queries with informative and coherent replies.

Downstream Use

Potential downstream applications include:

Fine-Tuning: Adapting the model for specific domains or industries by training on specialized datasets.
Integration into Multi-Modal Systems: Combining the model with other AI components, such as speech recognition or image processing modules, to create comprehensive interactive platforms.

Out-of-Scope Use

The model is not suitable for:

High-Stakes Decision Making: Scenarios where incorrect responses could lead to significant harm or financial loss.
Content Moderation: Reliably identifying or filtering sensitive or inappropriate content without human oversight.

Bias, Risks, and Limitations

While the MLC Model strives for accuracy and fairness, users should be aware of the following:

Biases: The model may reflect biases present in its training data, potentially leading to skewed or unbalanced responses.
Inappropriate Outputs: In certain contexts, the model might generate responses that are inappropriate or not aligned with user expectations.
Quantization Artifacts: The optimization process may introduce minor artifacts affecting response quality.

Recommendations

Human Oversight: Implement human-in-the-loop systems to review and moderate the model's outputs, especially in sensitive applications.
Regular Evaluation: Continuously assess the model's performance and update it with new data to mitigate biases and improve accuracy.
User Education: Inform users about the model's capabilities and limitations to set appropriate expectations.

How to Get Started with the Model

To utilize the MLC Model, you can employ the following Python code snippet using the MLC-LLM framework:

from mlc_llm import MLCEngine

# Initialize the MLCEngine with the Hugging Face URL
model_url = "HF://eccsm/mlc_llm"
engine = MLCEngine(model_url)

# Define the user prompt
prompt = "Hello! How can I assist you today?"

# Generate a response
response = ""
for output in engine.chat.completions.create(
    messages=[{"role": "user", "content": prompt}],
    stream=True,
):
    for choice in output.choices:
        response += choice.delta.get("content", "")

print(response)

# Terminate the engine after use
engine.terminate()

Training Details

Training Data

The MLC Model was fine-tuned on a diverse dataset comprising conversational data in English. The dataset includes dialogues from various domains to ensure a broad understanding of language and context.

Training Procedure

The fine-tuning process involved:

Preprocessing: Cleaning and tokenizing the text data to align with the model's input requirements.
Training Regime: Utilizing mixed-precision training to balance computational efficiency and model performance.
Hyperparameters:
- Batch Size: 32
- Learning Rate: 5e-5
- Epochs: 3

Evaluation

Testing Data

The model was evaluated on a separate validation set containing diverse conversational prompts to assess its generalization capabilities.

Metrics

Evaluation metrics included:

Perplexity: Measuring the model's ability to predict the next word in a sequence.
Response Coherence: Assessing the logical consistency of the model's replies.
Latency: Evaluating the time taken to generate responses, ensuring suitability for real-time applications.

Citation

If you utilize the MLC Model in your work, please cite it as follows:

@misc{mlc_model_2025,
  author = {Ekincan Casim},
  title = {MLC Model: A Conversational Language Model},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Repository},
  howpublished = {\url{https://huggingface.co/eccsm/mlc_llm}},
}

eccsm
/

mlc_llm