File size: 2,765 Bytes
ff26ff0
056adc2
 
 
 
ff26ff0
056adc2
 
 
 
ff26ff0
 
056adc2
 
 
 
 
 
 
ff26ff0
056adc2
 
 
 
 
ff26ff0
056adc2
ff26ff0
056adc2
ff26ff0
056adc2
 
ff26ff0
056adc2
 
 
ff26ff0
056adc2
 
 
 
ff26ff0
056adc2
 
 
 
 
 
 
 
 
ff26ff0
056adc2
 
 
 
 
 
 
 
 
 
 
 
 
ff26ff0
056adc2
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
---
license: apache-2.0
pipeline_tag: text-generation
language:
  - hi
tags:
- pretrained
inference:
  parameters:
    temperature: 0.7
---

# pranavajay/hindi-8b

## Overview

🌟 Introducing the "pranavajay/hindi-8b" model, a text generator  language model specifically fine-tuned for Hindi language conversational tasks. This model is designed to engage in fluent and contextually relevant conversations, making it an ideal tool for chatbots, language translation, and more.

## Model Details

- **Base Model**: A robust pre-trained language model, adapted for Hindi.
- **Fine-Tuning**: The model has been further fine-tuned on a diverse dataset of Hindi conversations to enhance its conversational capabilities.
- **Language Support**: Primarily Hindi, with the ability to understand and respond in contextually appropriate English when necessary.
- **Model Size**: 10.2B parameters for rich and nuanced responses.
- **Usage**: Ideal for building interactive AI applications that require natural language understanding and generation in Hindi.

## Quick Start

To use the "pranavajay/hindi-8b" model with the Hugging Face Transformers library, follow these steps:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("pranavajay/hindi-8b")
model = AutoModelForCausalLM.from_pretrained("pranavajay/hindi-8b")

# Example conversation
input_text = "हमारे देश का नवाब कौन है?"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=50, num_return_sequences=1)

# Decode the response
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(response)
```
Training
The "pranavajay/hindi-8b" model was trained using a combination of supervised learning and reinforcement learning techniques. The training dataset consisted of a wide range of Hindi conversations, including dialogues from various domains such as customer service, education, and entertainment.

License
The "pranavajay/hindi-8b" model is released under the Apache License 2.0. Please review the license for details regarding the use, modification, and distribution of the model.

Citation
If you use the "pranavajay/hindi-8b" model in your research or applications, please cite it as follows:

bibtex
```
@misc{hindi-8b,
  author = {Pranav Ajay},
  title = {Hindi Chat 8B Model},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Repository},
  howpublished = {\url{https://huggingface.co/pranavajay/hindi-chat-8b}}
}
```
Feedback and Support
For any questions, feedback, or support regarding the "pranavajay/hindi-chat-8b" model, please contact us at pranavajay74@gmail.com