0x_model0 ~82 million parameters
0x_model0 is a fine-tuned DistilGPT-2 language model designed for conversational and text generation tasks. Built on the lightweight DistilGPT-2 architecture, this model is efficient and easy to use for experimentation and basic chatbot applications.
Model Overview
- Base Model: DistilGPT-2 (pre-trained by Hugging Face)
- Fine-tuned on: A small, custom dataset of conversational examples.
- Framework: Hugging Face Transformers
- Use Cases:
- Simple conversational agents
- Text generation for prototyping
- Educational and research purposes
Features
1. Lightweight and Efficient
0x_model0 leverages the compact DistilGPT-2 architecture, offering fast inference and low resource requirements.
2. Custom Fine-tuning
The model has been fine-tuned on a modest dataset to adapt it for conversational tasks.
3. Basic Text Generation
Supports generation with standard features such as:
- Top-k Sampling
- Top-p Sampling (Nucleus Sampling)
- Temperature Scaling
Getting Started
Installation
To use 0x_model0, ensure you have Python 3.8+ and install the Hugging Face Transformers library:
Loading the Model
Load the model and tokenizer from Hugging Face's Model Hub:
1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3# Load the model and tokenizer
4tokenizer = AutoTokenizer.from_pretrained("MdJiyathKhan/0x_model0")
5model = AutoModelForCausalLM.from_pretrained("MdJiyathKhan/0x_model0")
6
7# Example usage
8input_text = "Hello, how can I assist you?"
9input_ids = tokenizer.encode(input_text, return_tensors="pt")
10outputs = model.generate(input_ids, max_length=100, top_k=50, top_p=0.9, temperature=0.7)
11response = tokenizer.decode(outputs[0], skip_special_tokens=True)
12print(response)
Interaction
You can create a simple chatbot or text generator using the model.
Model Performance
Limitations
While 0x_model0 is functional, it has limitations:
- Generates repetitive or incoherent responses in some scenarios.
- Struggles with complex or nuanced conversations.
- Outputs may lack factual accuracy.
This model is best suited for non-critical applications or educational purposes.
Training Details
Dataset
The model was fine-tuned on a basic dataset containing conversational examples.
Training Configuration
- Batch Size: 4
- Learning Rate: 5e-5
- Epochs: 2
- Optimizer: AdamW
- Mixed Precision Training: Enabled (FP16)
Hardware
Fine-tuning was performed on a single GPU with 4GB VRAM using PyTorch and Hugging Face Transformers.
