You can deploy this model and start earning money today!

RichardErkhov/
sohail2332_-_ai-chatbot2-gguf

Name: sohail2332_-_ai-chatbot2-gguf
Availability: InStock

endpoints_compatible

gguf

template

Views

No views yet

Quantization made by Richard Erkhov.

Github

Discord

Request more models

ai-chatbot2 - GGUF

Model creator: https://huggingface.co/sohail2332/
Original model: https://huggingface.co/sohail2332/ai-chatbot2/

Name	Quant method	Size
ai-chatbot2.Q2_K.gguf	Q2_K	0.08GB
ai-chatbot2.IQ3_XS.gguf	IQ3_XS	0.08GB
ai-chatbot2.IQ3_S.gguf	IQ3_S	0.08GB
ai-chatbot2.Q3_K_S.gguf	Q3_K_S	0.08GB
ai-chatbot2.IQ3_M.gguf	IQ3_M	0.09GB
ai-chatbot2.Q3_K.gguf	Q3_K	0.09GB
ai-chatbot2.Q3_K_M.gguf	Q3_K_M	0.09GB
ai-chatbot2.Q3_K_L.gguf	Q3_K_L	0.1GB
ai-chatbot2.IQ4_XS.gguf	IQ4_XS	0.1GB
ai-chatbot2.Q4_0.gguf	Q4_0	0.1GB
ai-chatbot2.IQ4_NL.gguf	IQ4_NL	0.1GB
ai-chatbot2.Q4_K_S.gguf	Q4_K_S	0.1GB
ai-chatbot2.Q4_K.gguf	Q4_K	0.11GB
ai-chatbot2.Q4_K_M.gguf	Q4_K_M	0.11GB
ai-chatbot2.Q4_1.gguf	Q4_1	0.11GB
ai-chatbot2.Q5_0.gguf	Q5_0	0.11GB
ai-chatbot2.Q5_K_S.gguf	Q5_K_S	0.11GB
ai-chatbot2.Q5_K.gguf	Q5_K	0.12GB
ai-chatbot2.Q5_K_M.gguf	Q5_K_M	0.12GB
ai-chatbot2.Q5_1.gguf	Q5_1	0.12GB
ai-chatbot2.Q6_K.gguf	Q6_K	0.13GB
ai-chatbot2.Q8_0.gguf	Q8_0	0.17GB

Original model description:

license: mit language:

en metrics:
bleu
perplexity base_model:
openai-community/gpt2 library_name: transformers

Model Card for Fine-Tuned GPT-2

Model Overview

This repository contains a fine-tuned version of the GPT-2 model. The model has been adapted to provide a conversational AI experience, suitable for chatbots and similar applications. It is designed to generate human-like text based on the input it receives.

Model Details

Model Type: Transformer-based language model
Base Model: GPT-2
Version: 1.0
Architecture: 12-layer transformer with 768 hidden units and 12 attention heads.

Intended Use

The model is intended for use in creating chatbots and conversational agents. It can generate responses to user inputs in a coherent and contextually relevant manner.

Examples of Use Cases

Customer support chatbots
Interactive storytelling applications
Personal assistants

Training Data

The model was fine-tuned on a diverse dataset that includes:

Dialogue from various sources, such as social media, forums, and chat transcripts.
Text data representative of conversational exchanges.

The dataset used for fine-tuning has been curated to ensure a wide range of topics and conversational styles.

Limitations

The model may generate incorrect or nonsensical answers in some situations.
It can exhibit biases present in the training data. Care should be taken to ensure ethical use, particularly in sensitive applications.
It may not understand highly specialized or technical topics unless trained on specific datasets.

How to Use

You can use this model via the Hugging Face Transformers library. Below is a sample code snippet to get you started:

python

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the fine-tuned model and tokenizer
model = GPT2LMHeadModel.from_pretrained("sohail2332/ai-chatbot2")
tokenizer = GPT2Tokenizer.from_pretrained("sohail2332/ai-chatbot2")

# Generate text
input_text = "Hello, how can I help you today?"
input_ids = tokenizer.encode(input_text, return_tensors='pt')

# Generate a response
output = model.generate(input_ids, max_length=50, num_return_sequences=1)
response = tokenizer.decode(output[0], skip_special_tokens=True)

print(response)

RichardErkhov/sohail2332_-_ai-chatbot2-gguf