learn-abc
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tatsu-lab_alpaca-tinyllama-chat-bnb-4bit

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 library_name: transformers
-tags: []
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 # Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
@@ -13,187 +13,87 @@ tags: []
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+tags: ["peft", "lora", "tinyllama", "text-generation", "fine-tuned"]
 ---
 # Model Card for Model ID
+This model is a fine-tuned version of `TinyLlama-1.1B-Chat-v1.0`, specialized for question-answering and summarization tasks related to the topic of DNA data storage. It was trained using the `PEFT` (Parameter-Efficient Fine-Tuning) method with `LoRA` adapters on a custom dataset `tatsu-lab/alpaca`.
 ### Model Description
+This is a fine-tuned language model based on the `TinyLlama-1.1B-Chat-v1.0` architecture. The model was trained to improve its ability to understand, summarize, and answer questions from text related to DNA data storage technology. It utilizes LoRA adapters, which makes the fine-tuned checkpoint small and efficient. This model is intended for research and educational purposes to explore the application of LLMs in niche, domain-specific tasks.
+- **Developed by:** Abhishek Singh
+- **Model type:** Causal language model
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0 (Inherits the license from the base model)
+- **Finetuned from model:** `TinyLlama/TinyLlama-1.1B-Chat-v1.0`
 ### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
+- **Repository:** [https://huggingface.co/your-username/tinyllama-fine-tuned-model](https://huggingface.co/your-username/tinyllama-fine-tuned-model)
 ## 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 model is intended to be used for the following purposes:
+* Summarizing key points from new documents or texts about DNA data storage.
+* Answering specific questions based on provided context regarding DNA data storage.
+* Generating short, informative explanations on the topic.
 ### Out-of-Scope Use
+This model is not suitable for:
+* General-purpose chat or conversational tasks on topics outside of DNA data storage.
+* Generating creative writing or essays.
+* Factual question-answering on general knowledge, as its knowledge is constrained to the fine-tuning data.
+### Bias, Risks, and Limitations
+This model has several limitations due to its specialized nature and small size:
+* Domain-Specific Knowledge: The model's knowledge is highly specialized. It may provide incorrect or nonsensical information (hallucinate) when asked about topics outside of DNA data storage.
+* Potential for Bias: The model inherits the biases of its base model, TinyLlama.
+* Simplicity: The model is not a substitute for expert advice or comprehensive research. It should be used as a supplementary tool for text analysis.
+## Recommendations
+Users should be aware of the model's limitations and verify any critical information it provides. It is recommended to use the model with a clear, specific prompt that includes relevant context for the best results.
+## How to Get Started with the Model
+You can get started with the model by loading it directly from the Hugging Face Hub using the `transformers` library.
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model_id = "your-username/tinyllama-fine-tuned-model"
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+# Example usage
+prompt = """### Instruction:
+Summarize the key findings from the provided text about DNA data storage.
+### Input:
+Deoxyribonucleic acid (DNA) has been successfully proposed as an advanced archival storage medium, due to its extraordinary data capacity and robust stability. ... (rest of your text here)
+### Response:
+"""
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
 ## Training Details
 ### Training Data
+The model was fine-tuned on a custom dataset containing text, summaries, and Q&A pairs related to the topic of DNA data storage. The dataset was formatted into a chat-like template with `### Instruction:`, `### Input:`, and `### Response:` sections.
 ### Training Procedure
+**1. Preprocessing**
+The training data was pre-processed into a chat template format to prepare it for the model. The tokenizer's pad_token was set to the eos_token to handle variable-length sequences.
+**2. Training Hyperparameters**
+* **Fine-tuning Method:** PEFT (LoRA)
+* **Training regime:** bf16 mixed precision
 ## Evaluation
+No formal evaluation metrics were calculated for this model. Its performance was qualitatively assessed by generating responses to prompts and checking for relevance and accuracy with respect to the fine-tuning data.