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metadata
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]

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 

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"])