qlora-mistral

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