QualityAI
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QAI-QDERM-1.5B

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 ---
-library_name: transformers
 tags:
-- unsloth
-- trl
-- sft
 ---
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 ---
+language: en
 tags:
+  - test-model
+  - reasoning
+  - unsloth
+  - fine-tuning
+  - small-model
 ---
+# 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:
+```python
+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)