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
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:
1from mlc_llm import MLCEngine
2
3# Initialize the MLCEngine with the Hugging Face URL
4model_url = "HF://eccsm/mlc_llm"
5engine = MLCEngine(model_url)
6
7# Define the user prompt
8prompt = "Hello! How can I assist you today?"
9
10# Generate a response
11response = ""
12for output in engine.chat.completions.create(
13 messages=[{"role": "user", "content": prompt}],
14 stream=True,
15):
16 for choice in output.choices:
17 response += choice.delta.get("content", "")
18
19print(response)
20
21# Terminate the engine after use
22engine.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:
1@misc{mlc_model_2025,
2 author = {Ekincan Casim},
3 title = {MLC Model: A Conversational Language Model},
4 year = {2025},
5 publisher = {Hugging Face},
6 journal = {Hugging Face Repository},
7 howpublished = {\url{https://huggingface.co/eccsm/mlc_llm}},
8}
