update Readme.md

README.md

@@ -2,205 +2,120 @@
 base_model: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
 library_name: peft
 ---
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for VAGOsolutions-Llama-3-SauerkrautLM-8b-Instruct-openassistant-guanaco
 
 ## Model Details
 
 ### Model Description
+This model is a fine-tuned version of **Llama-3-SauerkrautLM-8b**, optimized for **causal language modeling (CAUSAL_LM)** using **LoRA (Low-Rank Adaptation)**. The fine-tuning process was carried out under **Intel Gaudi access** using Habana Gaudi AI processors, leveraging `optimum-habana` for hardware acceleration.
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
+- **Developed by:** AHAMED-27
+- **Funded by:** [More Information Needed]
+- **Shared by:** AHAMED-27
+- **Model type:** Causal Language Model (CAUSAL_LM)
+- **Language(s):** English
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
+- **Finetuned from model:** [VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct](https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct)
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Model Sources
+- **Repository:** [AHAMED-27/VAGOsolutions-Llama-3-SauerkrautLM-8b-Instruct-openassistant-guanaco](https://huggingface.co/AHAMED-27/VAGOsolutions-Llama-3-SauerkrautLM-8b-Instruct-openassistant-guanaco)
+- **Paper:** [More Information Needed]
+- **Demo:** [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]
-
-### 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]
+This model is designed for natural language generation tasks, such as:
+- Text completion
+- Conversational AI
+- Story generation
+- Summarization
+
+### Downstream Use
+The model can be fine-tuned further for specific NLP applications such as:
+- Chatbots
+- Code generation
+- Sentiment analysis
+- Question answering
 
 ### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- The model is not intended for real-time decision-making applications where accuracy is critical.
+- Avoid using it for generating misinformation or harmful content.
 
 ## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+### Known Risks
+- The model may generate biased or incorrect responses as it is fine-tuned on publicly available datasets.
+- It may not perform well on low-resource languages or domain-specific tasks without additional fine-tuning.
 
 ### Recommendations
+- Users should verify the generated content before deploying it in production.
+- Ethical considerations should be taken into account while using this model.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## How to Get Started with the Model
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Use the code below to load and generate text using the model:
 
-## How to Get Started with the Model
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-Use the code below to get started with the model.
+tokenizer = AutoTokenizer.from_pretrained("AHAMED-27/VAGOsolutions-Llama-3-SauerkrautLM-8b-Instruct-openassistant-guanaco")
+model = AutoModelForCausalLM.from_pretrained("AHAMED-27/VAGOsolutions-Llama-3-SauerkrautLM-8b-Instruct-openassistant-guanaco")
 
-[More Information Needed]
+input_text = "Explain the benefits of using LoRA for fine-tuning large language models."
+inputs = tokenizer(input_text, return_tensors="pt")
+output = model.generate(**inputs)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
 
 ## Training Details
 
 ### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
+The model was fine-tuned on the **openassistant-guanaco** dataset.
 
 ### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
+#### Preprocessing
+- Tokenization was performed using the `AutoTokenizer` from the `transformers` library.
+- LoRA adaptation was applied to the attention projection layers (`q_proj`, `v_proj`).
 
 #### 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]
+- **Training Regime:** BF16 Mixed Precision
+- **Epochs:** 3
+- **Batch Size:** 16 per device
+- **Learning Rate:** 1e-4
+- **Optimizer:** Adam
+- **Scheduler:** Constant LR
+- **LoRA Rank (r):** 8
+- **LoRA Alpha:** 16
+- **LoRA Dropout:** 0.05
+
+#### Speeds, Sizes, Times
+- **Training Runtime:** 1086.32 seconds
+- **Training Samples per Second:** 16.197
+- **Training Steps per Second:** 1.015
+- **Total Available Memory:** 94.62 GB
+- **Max Memory Allocated:** 92.66 GB
+- **Memory Currently Allocated:** 67.67 GB
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
-
 #### Testing Data
+- The model was evaluated on a held-out validation set from the **openassistant-guanaco** dataset.
 
-<!-- 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]
-## Training procedure
-
+#### Evaluation Metrics
+- **Evaluation Accuracy:** 70.18%
+- **Evaluation Loss:** 1.4535
+- **Perplexity:** 4.28
+- **Evaluation Runtime:** 9.54 seconds
+- **Evaluation Samples per Second:** 35.85
+- **Evaluation Steps per Second:** 4.513
 
-### Framework versions
+## Software Dependencies
+- **Transformers Version:** 4.38.2
+- **Optimum-Habana Version:** 1.24.0
+- **Intel Gaudi SynapseAI Toolkit**
 
+## Acknowledgments
+This fine-tuning process was completed using **Intel Gaudi hardware**, enabling optimized performance with reduced training time. Special thanks to the **Intel Habana team** for their work on Gaudi AI processors.
 
-- PEFT 0.6.2
+For more details, visit [Habana Labs](https://habana.ai/).