Improve language tag

README.md

@@ -1,150 +1,162 @@
----
-datasets:
-- iimran/Medical-Intelligence-Questions
-base_model:
-- Qwen/Qwen2.5-3B
-language:
-- en
-tags:
-- medical
-- text-generation-inference
-- transformers
-- unsloth
----
-# Qwen2.5-3B-R1-MedicalReasoner
-
-**Qwen2.5-3B-R1-MedicalReasoner** is a clinical reasoning language model fine-tuned for advanced diagnostic and case-based problem solving. It has been developed for applications in medical education, clinical decision support, and research, with the capability to generate detailed chain-of-thought responses that include both the reasoning process and the final answer.
-
-## Overview
-
-- **Model Name:** Qwen2.5-3B-R1-MedicalReasoner  
-- **Base Architecture:** Qwen2.5 (3B)  
-- **Primary Application:** Clinical reasoning and medical problem solving  
-- **Key Features:**
-  - **Chain-of-Thought Outputs:** Responds with structured reasoning (`<reasoning> ... </reasoning>`) followed by a concise answer (`<answer> ... </answer>`).
-  - **Multi-Specialty Coverage:** Well-suited for scenarios in internal medicine, surgery, pediatrics, OB/GYN, emergency medicine, and more.
-  - **Explainable AI:** Generates detailed, educational explanations that support clinical decision-making.
-
-## Model Capabilities
-
-- **Expert-Level Clinical Reasoning:** Equipped to analyze complex clinical scenarios and provide in-depth diagnostic reasoning.
-- **Structured Outputs:** Enforces a response format that separates the thought process from the final answer, aiding transparency and interpretability.
-- **Optimized for Speed:** Uses Unsloth and vLLM for fast, efficient inference on GPU systems.
-
-## Inference and Usage
-
-Below is an example of how to use the model for inference or refer to inference.py in files section:
-
-```python
-from unsloth import FastLanguageModel, is_bfloat16_supported
-from vllm import SamplingParams
-from huggingface_hub import snapshot_download
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name="iimran/Qwen2.5-3B-R1-MedicalReasoner",
-    load_in_4bit=True,
-    fast_inference=True,
-    gpu_memory_utilization=0.5
-)
-lora_rank = 64
-model = FastLanguageModel.get_peft_model(
-    model,
-    r=lora_rank,
-    target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
-                    "gate_proj", "up_proj", "down_proj"],
-    lora_alpha=lora_rank,
-    use_gradient_checkpointing="unsloth",
-    random_state=3407,
-)
-lora_path = snapshot_download("iimran/Qwen2.5-3B-R1-MedicalReasoner-lora-adapter")
-print("LoRA adapter downloaded to:", lora_path)
-model.load_lora(lora_path)
-SYSTEM_PROMPT = (
-    "Respond in the following format:\n"
-    "<reasoning>\n"
-    "...\n"
-    "</reasoning>\n"
-    "<answer>\n"
-    "...\n"
-    "</answer>"
-)
-USER_PROMPT = (
-    "In the context of disseminated intravascular coagulation (DIC), "
-    "which blood component is expected to show an increase due to the excessive breakdown of fibrin?"
-)
-text = tokenizer.apply_chat_template(
-    [
-        {"role": "system", "content": SYSTEM_PROMPT},
-        {"role": "user", "content": USER_PROMPT},
-    ],
-    tokenize=False,
-    add_generation_prompt=True
-)
-sampling_params = SamplingParams(
-    temperature=0.1,
-    top_p=0.95,
-    max_tokens=4096,
-)
-outputs = model.fast_generate(
-    text,
-    sampling_params=sampling_params,
-    lora_request=None
-)
-print(outputs[0].outputs[0].text)
-```
-
-### Adapter Integration
-
-For further fine-tuning or experiments with LoRA adapters, the LoRA adapter for this model is available in a separate repository.
-
-- **LoRA Adapter Repo:** [iimran/Qwen2.5-3B-R1-MedicalReasoner-lora-adapter](https://huggingface.co/iimran/Qwen2.5-3B-R1-MedicalReasoner-lora-adapter)
-
-To download and integrate the LoRA adapter:
-```python
-from huggingface_hub import snapshot_download
-
-# Download the LoRA adapter repository:
-lora_path = snapshot_download("iimran/Qwen2.5-3B-R1-MedicalReasoner-lora-adapter")
-print("LoRA adapter downloaded to:", lora_path)
-
-# Load the adapter into the model:
-model.load_lora(lora_path)
-```
-
-## Installation
-
-To use this model, install the required packages:
-```bash
-pip install unsloth vllm trl datasets huggingface-hub
-```
-
-A compatible GPU is recommended for optimal performance.
-
-## Citation
-
-If you use **Qwen2.5-3B-R1-MedicalReasoner** in your research, please cite:
-```bibtex
-@misc{sarwar2025reinforcement,
-  author = {Imran Sarwar and Muhammad Rouf Mustafa},
-  title = {Reinforcement Learning Elevates Qwen2.5-3B Medical Reasoning Performance},
-  year = {2025},
-  month = {Apr},
-  day = {10},
-  publisher = {Imran Sarwar's Blog},
-  howpublished = {\url{https://www.imransarwar.com/blog-posts/Reinforcement-Learning-Elevates-Qwen2.5-Medical-Reasoning-Performance.html}},
-  note = {Accessed: 2025-04-09}
-}
-```
-
-```bibtex
-@misc{Qwen2.5-3B-R1-MedicalReasoner,
-  authors = {Imran Sarwar, Muhammad Rouf Mustafa},
-  title = {Qwen 2.5-3B Meets Deepseek R1: A Fine-Tuned Medical Reasoning Model for Enhanced Diagnostics},
-  year = {2025},
-  publisher = {Hugging Face},
-  url = {https://huggingface.co/iimran/Qwen2.5-3B-R1-MedicalReasoner}
-}
-```
-
-## Disclaimer
-
+---
+datasets:
+- iimran/Medical-Intelligence-Questions
+base_model:
+- Qwen/Qwen2.5-3B
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+tags:
+- medical
+- text-generation-inference
+- transformers
+- unsloth
+---
+# Qwen2.5-3B-R1-MedicalReasoner
+
+**Qwen2.5-3B-R1-MedicalReasoner** is a clinical reasoning language model fine-tuned for advanced diagnostic and case-based problem solving. It has been developed for applications in medical education, clinical decision support, and research, with the capability to generate detailed chain-of-thought responses that include both the reasoning process and the final answer.
+
+## Overview
+
+- **Model Name:** Qwen2.5-3B-R1-MedicalReasoner  
+- **Base Architecture:** Qwen2.5 (3B)  
+- **Primary Application:** Clinical reasoning and medical problem solving  
+- **Key Features:**
+  - **Chain-of-Thought Outputs:** Responds with structured reasoning (`<reasoning> ... </reasoning>`) followed by a concise answer (`<answer> ... </answer>`).
+  - **Multi-Specialty Coverage:** Well-suited for scenarios in internal medicine, surgery, pediatrics, OB/GYN, emergency medicine, and more.
+  - **Explainable AI:** Generates detailed, educational explanations that support clinical decision-making.
+
+## Model Capabilities
+
+- **Expert-Level Clinical Reasoning:** Equipped to analyze complex clinical scenarios and provide in-depth diagnostic reasoning.
+- **Structured Outputs:** Enforces a response format that separates the thought process from the final answer, aiding transparency and interpretability.
+- **Optimized for Speed:** Uses Unsloth and vLLM for fast, efficient inference on GPU systems.
+
+## Inference and Usage
+
+Below is an example of how to use the model for inference or refer to inference.py in files section:
+
+```python
+from unsloth import FastLanguageModel, is_bfloat16_supported
+from vllm import SamplingParams
+from huggingface_hub import snapshot_download
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="iimran/Qwen2.5-3B-R1-MedicalReasoner",
+    load_in_4bit=True,
+    fast_inference=True,
+    gpu_memory_utilization=0.5
+)
+lora_rank = 64
+model = FastLanguageModel.get_peft_model(
+    model,
+    r=lora_rank,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
+                    "gate_proj", "up_proj", "down_proj"],
+    lora_alpha=lora_rank,
+    use_gradient_checkpointing="unsloth",
+    random_state=3407,
+)
+lora_path = snapshot_download("iimran/Qwen2.5-3B-R1-MedicalReasoner-lora-adapter")
+print("LoRA adapter downloaded to:", lora_path)
+model.load_lora(lora_path)
+SYSTEM_PROMPT = (
+    "Respond in the following format:\n"
+    "<reasoning>\n"
+    "...\n"
+    "</reasoning>\n"
+    "<answer>\n"
+    "...\n"
+    "</answer>"
+)
+USER_PROMPT = (
+    "In the context of disseminated intravascular coagulation (DIC), "
+    "which blood component is expected to show an increase due to the excessive breakdown of fibrin?"
+)
+text = tokenizer.apply_chat_template(
+    [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user", "content": USER_PROMPT},
+    ],
+    tokenize=False,
+    add_generation_prompt=True
+)
+sampling_params = SamplingParams(
+    temperature=0.1,
+    top_p=0.95,
+    max_tokens=4096,
+)
+outputs = model.fast_generate(
+    text,
+    sampling_params=sampling_params,
+    lora_request=None
+)
+print(outputs[0].outputs[0].text)
+```
+
+### Adapter Integration
+
+For further fine-tuning or experiments with LoRA adapters, the LoRA adapter for this model is available in a separate repository.
+
+- **LoRA Adapter Repo:** [iimran/Qwen2.5-3B-R1-MedicalReasoner-lora-adapter](https://huggingface.co/iimran/Qwen2.5-3B-R1-MedicalReasoner-lora-adapter)
+
+To download and integrate the LoRA adapter:
+```python
+from huggingface_hub import snapshot_download
+
+# Download the LoRA adapter repository:
+lora_path = snapshot_download("iimran/Qwen2.5-3B-R1-MedicalReasoner-lora-adapter")
+print("LoRA adapter downloaded to:", lora_path)
+
+# Load the adapter into the model:
+model.load_lora(lora_path)
+```
+
+## Installation
+
+To use this model, install the required packages:
+```bash
+pip install unsloth vllm trl datasets huggingface-hub
+```
+
+A compatible GPU is recommended for optimal performance.
+
+## Citation
+
+If you use **Qwen2.5-3B-R1-MedicalReasoner** in your research, please cite:
+```bibtex
+@misc{sarwar2025reinforcement,
+  author = {Imran Sarwar and Muhammad Rouf Mustafa},
+  title = {Reinforcement Learning Elevates Qwen2.5-3B Medical Reasoning Performance},
+  year = {2025},
+  month = {Apr},
+  day = {10},
+  publisher = {Imran Sarwar's Blog},
+  howpublished = {\url{https://www.imransarwar.com/blog-posts/Reinforcement-Learning-Elevates-Qwen2.5-Medical-Reasoning-Performance.html}},
+  note = {Accessed: 2025-04-09}
+}
+```
+
+```bibtex
+@misc{Qwen2.5-3B-R1-MedicalReasoner,
+  authors = {Imran Sarwar, Muhammad Rouf Mustafa},
+  title = {Qwen 2.5-3B Meets Deepseek R1: A Fine-Tuned Medical Reasoning Model for Enhanced Diagnostics},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/iimran/Qwen2.5-3B-R1-MedicalReasoner}
+}
+```
+
+## Disclaimer
+
 This model is intended for research and educational purposes only. It should not be used as the sole basis for clinical decision-making. All outputs should be validated by qualified healthcare professionals.