Create README.md

README.md

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+This model card provides details on a fine-tuned version of Savianto/qlora-mistral, a language model trained using the QLoRA technique on conversational data for enhanced text generation, particularly in question-answering and conversational tasks.
+## Model Details
+
+### Model Description
+
+Model Description
+This is a fine-tuned version of the Savianto/qlora-mistral model using the QLoRA technique. The fine-tuning was done to improve the model’s ability to generate coherent and context-aware responses in conversational and question-answering tasks. QLoRA allows for efficient fine-tuning of large models while optimizing for memory usage.
+
+Developed by: Yash Sawant
+Model type: Causal Language Model (AutoModelForCausalLM)
+Language(s) (NLP): English
+License: [Specify License Type Here]
+Finetuned from model: Savianto/qlora-mistral
+
+
+
+- **Developed by:** Yash Sawant
+- **Model type:** Causal Language Model (AutoModelForCausalLM)
+- **Language(s) (NLP):** English
+- **Finetuned from model [optional]:** teknium/OpenHermes-2-Mistral-7B
+
+
+## Uses
+
+This model can be directly used for:
+
+Question answering
+Conversational agents (chatbots)
+Text generation tasks (summarization, text completion)
+
+### Direct Use
+
+This model can be fine-tuned further for specific tasks such as:
+
+Domain-specific question answering
+Custom chatbot agents
+Document summarization
+
+
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the fine-tuned model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("Savianto/qlora-mistral-finetuned")
+tokenizer = AutoTokenizer.from_pretrained("Savianto/qlora-mistral-finetuned")
+
+# Example prompt
+prompt = "What is the capital of France?"
+
+# Tokenize and generate output
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+output = model.generate(input_ids, max_length=50)
+
+# Decode the response
+response = tokenizer.decode(output[0], skip_special_tokens=True)
+print(response)
+
+[More Information Needed]
+
+## Training Details
+Training Details
+Training Data
+The model was fine-tuned using a conversational dataset, focusing on question-answer pairs and dialogue examples. This enhances the model's ability to generate contextually relevant and coherent responses.
+
+Training Procedure
+Hardware: GPU (NVIDIA A100, 40GB)
+Training Time: 5 epochs with early stopping
+Optimizer: AdamW
+Learning Rate: 2e-5
+Batch Size: 16
+Training regime: Mixed Precision (fp16)
+Preprocessing
+Tokenized the input text with padding and truncation for consistent input lengths.
+Speeds, Sizes, Times
+Training Time: ~3 hours
+Model Size: ~7B parameters (Base Model: Savianto/qlora-mistral)
+Evaluation
+Testing Data
+The model was evaluated on a validation split of the fine-tuning dataset, with question-answer pairs and conversational exchanges.
+
+Metrics
+Perplexity: Evaluated using standard perplexity for text generation models.
+Coherence: Human-evaluated coherence in generated responses.
+Results
+The model exhibited low perplexity scores on the validation set and performed well in conversational coherence during testing.
+
+Summary
+The model is well-suited for question-answering tasks, conversational agents, and general text generation tasks but may require additional tuning for domain-specific applications.
+
+Model Examination
+No further interpretability analysis was conducted on this model.
+
+Environmental Impact
+Carbon emissions for this model can be estimated using the Machine Learning Impact calculator based on the following parameters:
+
+Hardware Type: NVIDIA A100
+Training Hours: ~3 hours
+Cloud Provider: Google Cloud
+Compute Region: US-Central
+Carbon Emitted: 0.98 kg CO2eq (estimated)
+Technical Specifications
+Model Architecture and Objective
+This model is based on Mistral architecture, with the objective of generating coherent and contextually aware responses in conversation and question-answering tasks.
+
+Compute Infrastructure
+Hardware
+NVIDIA A100 40GB GPU
+Software
+Python 3.8
+Transformers (Hugging Face) v4.x
+PyTorch 1.10+
+Accelerate
+
+
+
+