README.md

---
library_name: transformers
tags: [causal-lm, lora, fine-tuned, qwen, deepseek]
---

# Model Card for Qwen-1.5B-LoRA-philosophy

This model is a LoRA-fine-tuned causal language model based on `deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B`. It was trained on a custom philosophy dataset with fields `"prompt"` and `"completion"`.

## Model Details

### Model Description

A parameter-efficient fine-tuning of a 1.5B-parameter Qwen-based model.  
At inference time, you can feed it a text prompt and it will generate the continuation.

- **Developed by:**  
- **Funded by [optional]:**  
- **Shared by [optional]:**  
- **Model type:** Causal Language Model (LM) with LoRA adapters  
- **Language(s) (NLP):** English  
- **License:**  
- **Finetuned from model [optional]:** deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

### Model Sources [optional]

- **Repository:** https://huggingface.co/your-username/small-fine-tunes  
- **Paper [optional]:**  
- **Demo [optional]:**  

## Uses

### Direct Use

This model can be used out-of-the-box for text generation tasks such as chatbots, text completion, and conversational AI workflows.

### Downstream Use [optional]

Developers can further fine-tune or adapt the model for domain-specific conversation, question answering, or summarization tasks.

### Out-of-Scope Use

- High-stakes decision making without human oversight  
- Generation of disallowed or sensitive content  
- Real-time safety-critical systems  

## Bias, Risks, and Limitations

Since the base model and the fine-tuning data are proprietary or custom, unknown biases may exist. The model may:

- Produce incorrect or hallucinatory statements  
- Reflect biases present in the source data  

### Recommendations

- Always review generated text for factual accuracy.  
- Do not rely on this model for safety-critical applications without additional guardrails.  

## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

tokenizer = AutoTokenizer.from_pretrained("your-username/small-fine-tunes")
model = AutoModelForCausalLM.from_pretrained("your-username/small-fine-tunes")

generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
output = generator("Once upon a time", max_length=100)
print(output[0]["generated_text"])