Update README.md

README.md

@@ -1,202 +1,114 @@
 ---
 base_model: CohereForAI/aya-23-8B
 library_name: peft
+license: apache-2.0
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for Aya Fine-Tuned Model
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
+This model is a fine-tuned version of the `CohereForAI/aya-23-8B` base model. It has been fine-tuned using a private dataset of prompt-response pairs that has been curated over the past two years. The fine-tuning process aimed to improve the model's ability to generate relevant and accurate responses in various conversational contexts.
 
-- **Developed by:** [More Information Needed]
+- **Developed by:** Franck Stéphane NDZOMGA
 - **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Shared by [optional]:** Franck Stéphane NDZOMGA
+- **Model type:** Causal Language Model with LoRA Adapters
+- **Language(s) (NLP):** Primarily English (add other languages if applicable)
+- **License:** Apache-2.0
+- **Finetuned from model:** CohereForAI/aya-23-8B
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
+- **Repository:** [Include the repository link here if publicly available]
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+This model can be used directly for text generation tasks, such as chatbots, automated customer support, or other conversational AI applications. It can generate responses based on provided prompts, mimicking human-like conversational patterns.
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be further fine-tuned for specific tasks or integrated into larger systems that require contextual understanding and response generation capabilities. Examples include virtual assistants, personalized content generation, and more.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is not suitable for tasks requiring factual accuracy and should not be used in applications where generating misinformation could cause harm. Misuse in generating offensive, harmful, or biased content is strongly discouraged.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+As with many language models, this fine-tuned model may exhibit biases present in the training data. The responses might reflect the specific contexts and perspectives inherent to the dataset it was trained on. Users should be aware of these potential biases and use the model with caution, especially in sensitive or critical applications.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users should monitor the outputs of the model and implement additional filtering or moderation systems as necessary. Awareness of the model's limitations and potential biases is crucial for responsible deployment.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+To use this model for text generation, you can load it using the Hugging Face `transformers` library:
 
-[More Information Needed]
+```python
+# pip install transformers==4.41.1
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-## Training Details
+model_id = "fsndzomga/aya-finetuned-mura-8B-lora"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
 
-### Training Data
+# Format message with the command-r-plus chat template
+messages = [{"role": "user", "content": "who is emmanuel macron ?"}]
+input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+gen_tokens = model.generate(
+    input_ids, 
+    max_new_tokens=100, 
+    do_sample=True, 
+    temperature=0.3,
+    )
 
-[More Information Needed]
-
-### Training Procedure
+gen_text = tokenizer.decode(gen_tokens[0])
+print(gen_text)
+```
+## Training Details
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+### Training Data
 
-#### Preprocessing [optional]
+The model was fine-tuned using a private dataset of prompt-response pairs curated by Franck Stéphane NDZOMGA over the past two years. The dataset includes a diverse set of conversational examples designed to improve the model's response generation capabilities.
 
-[More Information Needed]
+### Training Procedure
 
+- **Preprocessing [optional]:** The dataset was preprocessed to ensure consistent formatting, including tokenization using the base model's tokenizer and normalization of text inputs.
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.12.0
+- **Precision:** Mixed precision (fp16)
+- **Number of epochs:** [Specify the number of epochs]
+- **Batch size:** 1 (gradient accumulation steps: 16 to handle memory issues)
+- **Learning rate:** 5e-5
+- **Warmup steps:** 100
+- **Weight decay:** 0.01
+- **Logging:** Every 10 steps
+- **Checkpoint saving:** Every 50 steps, keeping only the latest 2 checkpoints
+- **Evaluation:** Every 50 steps
+- **Max steps:** 100
+- **Remove unused columns:** False
+- **Mixed Precision:** Disabled (fp16=False) to avoid conflicts
+
+### Speeds, Sizes, Times [optional]
+
+- **Training started:** [Date]
+- **Training completed:** [Date]
+- **Average training speed:** [Specify if available]
+- **Model size:** [Specify if available]
+
+### Additional Information from Training Code
+
+- The training utilized the PEFT (Parameter Efficient Fine-Tuning) library, specifically leveraging the LoRA (Low-Rank Adaptation) method to fine-tune the `CohereForAI/aya-23-8B` model.
+- The model was loaded with 8-bit precision to manage memory more effectively during training.
+- GPU memory management strategies were employed, including setting environment variables to manage CUDA memory allocation and clearing the PyTorch cache.
+- The training process involved using the `Hugging Face` ecosystem for model storage and deployment, and all model files were pushed to the Hugging Face Hub for accessibility.