safetensors qwen2 region:us
---
{}
---
base_model:
- StevesInfinityDrive/DeepSeek-R1-Distill-Qwen-1.0B
library_name: peft
---

# Model Card for DeepSeek-R1-Distill-Qwen-1.0B

## Model Details

### Model Description

DeepSeek-R1-Distill-Qwen-1.0B is a distilled version of the DeepSeek-R1 model, designed for efficiency while maintaining strong performance across various NLP tasks. The model has been fine-tuned using PEFT (Parameter Efficient Fine-Tuning) to optimize its ability for downstream applications, particularly in chatbot interactions, document summarization, and contextual understanding.

- **Developed by:** StevesInfinityDrive  
- **Funded by [optional]:** [More Information Needed]  
- **Shared by [optional]:** StevesInfinityDrive  
- **Model type:** Distilled Transformer-based Language Model  
- **Language(s) (NLP):** English, Chinese (potential multilingual capability)  
- **License:** [More Information Needed]  
- **Finetuned from model [optional]:** DeepSeek-R1-Qwen-1.0B  

### Model Sources

- **Repository:** [More Information Needed]  
- **Paper [optional]:** [More Information Needed]  
- **Demo [optional]:** [More Information Needed]  

## Uses

### Direct Use

This model can be used directly for NLP tasks such as:  
- Chatbot applications  
- Summarization and content generation  
- Code completion and assistance  
- Sentiment analysis  
- General language understanding tasks  

### Downstream Use

Fine-tuning this model with PEFT allows for customization in:  
- Domain-specific NLP applications (e.g., legal, medical, finance)  
- Personalized AI assistants  
- Specialized chatbots  

### Out-of-Scope Use

- Not suitable for real-time high-accuracy requirements without further fine-tuning.  
- Should not be used for generating biased, unethical, or misleading content.  
- The model may have limitations in highly technical or niche domain-specific queries.  

## Bias, Risks, and Limitations

- The model may exhibit biases present in its training data.  
- It may generate hallucinated or incorrect responses.  
- Limited interpretability in decision-making processes.  

### Recommendations

Users should carefully evaluate outputs, especially in critical applications such as healthcare, law, or finance. Mitigating bias through additional fine-tuning and prompt engineering is recommended.

## How to Get Started with the Model

Use the following code to load the model:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "StevesInfinityDrive/DeepSeek-R1-Distill-Qwen-1.0B"
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model)

peft_model = PeftModel.from_pretrained(model, base_model)

## Training Data

- Distilled using knowledge distillation techniques from DeepSeek-R1-Qwen-1.0B.
- Includes a mixture of publicly available datasets for NLP tasks.
- Additional details on preprocessing and fine-tuning pipelines are needed.

## Training Procedure

### Preprocessing

- Standard NLP tokenization and text cleaning applied.

### Training Hyperparameters

- Precision: fp16 mixed precision
- Batch Size: [More Information Needed]
- Learning Rate: [More Information Needed]
- Training Steps: [More Information Needed]

### Speeds, Sizes, Times

- Model size: 1B parameters

## Evaluation

### Testing Data, Factors & Metrics

## Testing Data

- Evaluated on standard NLP benchmarks

### Factors

- Performance varies depending on the prompt complexity and domain
- Bias detection and mitigation strategies are still under analysis

### Metrics

- Perplexity
- BLEU Score
- F1 Score (for classification tasks)

### Summary

The model shows strong performance for general NLP tasks but may require domain-specific fine-tuning for optimal performance.

## Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

## Technical Specifications

### Model Architecture and Objective

- Transformer-based autoregressive model
- Optimized for inference efficiency through distillation
- Parameter-efficient fine-tuning via PEFT

## Compute Infrastructure

### Software

Framework: PyTorch
Libraries: transformers, peft, accelerate
Compatible with Hugging Face API