README.md

---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
library_name: peft
pipeline_tag: text-generation
language: en
tags:
- deepseek
- text-generation
- conversational
---

# DeepSeek Chatbot

This is a fine-tuned version of DeepSeek-R1-Distill-Qwen-1.5B, optimized for conversational AI applications. The model maintains the base model's capabilities while being tuned for improved dialogue interactions.

## Model Details

### Model Description

- **Developed by:** Trinoid
- **Model type:** Conversational Language Model
- **Language(s):** English
- **License:** Same as base model (DeepSeek-R1-Distill-Qwen-1.5B)
- **Finetuned from model:** deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

## Uses

### Direct Use

This model can be used for:
- General conversation
- Text generation
- Question answering
- Chat-based applications

Example usage:
```python
from huggingface_hub import InferenceClient

client = InferenceClient("Trinoid/Deepseek_Chatbot")

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello, how are you?"}
]

response = client.chat_completion(
    messages,
    max_tokens=512,
    temperature=0.7,
    top_p=0.95
)
```

### Out-of-Scope Use

This model should not be used for:
- Generation of harmful or malicious content
- Spreading misinformation
- Production of illegal content
- Making critical decisions without human oversight

## Training Details

### Training Procedure

#### Training Hyperparameters

- **Training regime:** fp16 mixed precision
- **Framework:** PEFT (Parameter-Efficient Fine-Tuning)
- **PEFT Method:** LoRA
- **Version:** PEFT 0.14.0

## Technical Specifications

### Model Architecture and Objective

- Base architecture: DeepSeek-R1-Distill-Qwen-1.5B
- Fine-tuning method: PEFT/LoRA
- Primary objective: Conversational AI

### Compute Infrastructure

#### Software
- PEFT 0.14.0
- Transformers
- Python 3.x

## Model Card Contact

For questions or issues about this model, please open an issue in the model repository.