QAI-QDERM-1.5B Test Model

Model Name: QAI-QDERM-1.5B
Base Model: unsloth/DeepSeek-R1-Distill-Qwen-1.5B-bnb-4bit
Framework: Transformers, Unsloth, TRL
Architecture: Transformer-based language model
Quantization: 4-bit (using bitsandbytes)
Trainable Parameters: ~50M (via LoRA adapters)

Intended Use

This model is a test model designed for research purposes. It is optimized to:

• Test Reasoning: Evaluate chain-of-thought reasoning and step-by-step problem solving.
• Rapid Prototyping: Serve as a lightweight platform for experimentation with domain-specific tasks such as dermatology Q&A and medical reasoning.
• Parameter-Efficient Fine-Tuning: Demonstrate the effectiveness of LoRA-based fine-tuning on a smaller model.

Note: This model is not intended for production use.

Training Details

• Datasets:

  • Dermatology Question Answer Dataset (Mreeb/Dermatology-Question-Answer-Dataset-For-Fine-Tuning)
  • Medical Reasoning SFT Dataset (FreedomIntelligence/medical-o1-reasoning-SFT)

• Training Strategy:
  A two-stage fine-tuning process was used:

  1. Stage 1: Fine-tuning on Dermatology Q&A data.
  2. Stage 2: Further fine-tuning on Medical Reasoning data.

• Fine-Tuning Method:
  Parameter-efficient fine-tuning using LoRA via Unsloth, updating approximately 18 million parameters.

• Hyperparameters:

  • Stage 1: Learning rate ≈ 2e-4, effective batch size of 8 (per-device batch size 2, gradient accumulation steps 4), and a total of 546 training steps.
  • Stage 2: Further fine-tuning with a lower learning rate (≈ 3e-5) and controlled via max_steps (e.g., 1500 steps) for additional refinement.

Evaluation & Performance

• Metrics:
  Training loss was monitored during fine-tuning, and qualitative assessments were made on reasoning prompts and Q&A tasks.
• Observations:
  • The model shows promising chain-of-thought reasoning ability on test prompts.
  • As a small test model, its performance is intended to be a baseline for further experimentation and is not expected to match larger production models.

Limitations

• Scale: Due to its small size, the model may struggle with very complex reasoning tasks.
• Data: The limited domain-specific fine-tuning data may result in occasional inaccuracies.
• Intended Use: This model is for research and testing purposes only.

Inference Example

Below is an example of how to run inference with this model:

from unsloth import FastLanguageModel
from transformers import AutoTokenizer

# Load the fine-tuned model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    "your_hf_username/unsloth_final_model",  # Replace with your model repo
    max_seq_length=2048,
    load_in_4bit=True,
    device_map="auto"
)

# Enable fast inference mode
FastLanguageModel.for_inference(model)

# Define a prompt
prompt = "Explain the concept of psoriasis and its common symptoms."

# Tokenize and generate a response
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=150, use_cache=True)

# Decode and print the result
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Generated Output:", result)