README.md

---
license: mit
language:
- en
base_model:
- togethercomputer/RedPajama-INCITE-Chat-3B-v1
---

# 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](https://huggingface.co/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](https://mlc.ai/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](https://huggingface.co/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:

```python
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:

```bibtex
@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}},
}
```