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README.md

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----
-license: mit
----
+---
+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}},
+}
+```
+