Rustamshry
/

Qwen2.5-1.5B-Medical-Az

@@ -1,202 +1,133 @@
 ---
 base_model: unsloth/Qwen2.5-1.5B-Instruct
 library_name: peft
 ---
 # Model Card for Model ID
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 ### Framework versions
 - PEFT 0.14.0

 ---
 base_model: unsloth/Qwen2.5-1.5B-Instruct
 library_name: peft
+license: mit
+datasets:
+- Rustamshry/medical_o1_reasoning_SFT_az
+language:
+- az
+pipeline_tag: question-answering
+tags:
+- biology
+- medical
 ---
 # Model Card for Model ID
 ## Model Details
+### Model Description
+This model is a fine-tuned version of Qwen2.5-1.5B-Instruct on an Azerbaijani medical reasoning dataset.
+It is designed to understand complex medical instructions, interpret clinical cases, and generate informed answers in Azerbaijani.
+- **Developed by:** Rustam Shiriyev
+- **Model type:** Causal Language Model
+- **Language(s) (NLP):** Azerbaijani
+- **License:** MIT
+- **Finetuned from model:** unsloth/Qwen2.5-1.5B-Instruct
+- **Fine-tuning Method:** Supervised Fine-Tuning (SFT) using Unsloth + LoRA
+- **Domain:** Medical Question Answering / Reasoning
+- **Dataset:** The training data consists of ~19,696 rows, translated from the FreedomIntelligence/medical-o1-reasoning-SFT dataset
 ## Uses
 ### Direct Use
+You can use this model directly for:
+- Medical QA tasks in Azerbaijani
+- Evaluating LLMs' ability to reason about clinical data in low-resource languages
+- Generating educational prompts or tutoring-style medical answers
+- Research on instruction tuning and localization of medical language models
 ### Out-of-Scope Use
+- Use in life-critical medical applications
+- Any application where incorrect answers could cause harm
+- Use by patients or non-medical professionals for self-diagnosis
+- Deployment in commercial healthcare systems without regulatory oversight or expert validation
 ## Bias, Risks, and Limitations
+The model has not been clinically validated and must not be used for real medical decision-making.
+Trained only on a single-source dataset, so it may not generalize to all medical topics.
+Performance in zero-shot generalisation (e.g., English → Azerbaijani medical transfer) has not been tested.
 ## How to Get Started with the Model
+```python
+login(token="")
+tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen2.5-1.5B-Instruct",)
+base_model = AutoModelForCausalLM.from_pretrained(
+    "unsloth/Qwen2.5-1.5B-Instruct",
+    device_map="auto", token=""
+)
+model = PeftModel.from_pretrained(base_model,"Rustamshry/Qwen2.5-1.5B-Medical-Az")
+instruction = "45 yaşlı kişi qəfil danışıqda pozulma, yeriyişində dəyişiklik və titrəmə meydana gəlir. Ən ehtimal diaqnoz nədir?"
+prompt = f"""### Instruction:\n{instruction}\n\n### Response:\n"""
+input_ids = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **input_ids,
+    max_new_tokens=1024,
+    #temperature=0.7,
+    #top_p=0.9,
+    #do_sample=True,
+    #eos_token_id=tokenizer.eos_token_id
+)
+print(tokenizer.decode(outputs[0]))
+```
+## Training Details
+### Training Data
+The model was fine-tuned on a translated and cleaned version of FreedomIntelligence/medical-o1-reasoning-SFT, which was manually converted into Azerbaijani.
+All examples were filtered for translation quality and medical relevance.
+Dataset(Translated): Rustamshry/medical_o1_reasoning_SFT_az
+Link of Original Dataset: huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT
+### Training Procedure
+The model was trained using supervised fine-tuning (SFT) with parameter-efficient fine-tuning (PEFT) via LoRA, using the Unsloth library for memory-optimized training.
+- **Training regime:** fp16
+- **Epochs:** 2
+- **Batch size:** 2
+- **Gradient accumulation steps:** 4
+- **Max sequence lenght:** 2000
+- **Learning rate:** 2e-5
+- **Optimizer:** adamw_torch
+- **fp16:** True
+- **LoRa rank:** 6
+- **Aplha:** 16
+- **Target Modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+#### Speeds, Sizes, Times [optional]
+- **Training speed:** 0.12 steps/sec
+- **Total training time:** 11 hours, 26 minutes
+- **Total training steps:** 4924
 #### Hardware
+-**GPUs Used:**. NVIDIA Tesla T4 GPUs via Kaggle Notebook
 ### Framework versions
 - PEFT 0.14.0