Update README.md

README.md

@@ -1,135 +1,135 @@
----
-datasets:
-- starfishdata/playground_endocronology_notes_1500
-metrics:
-- bertscore
-- bleurt
-- rouge
-library_name: transformers
-base_model:
-- unsloth/Llama-3.2-1B-Instruct
-license: apache-2.0
-language:
-- en
----
-
-## Model Details
-*   **Base Model:** [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
-*   **Fine-tuning Method:** PEFT (Parameter-Efficient Fine-Tuning) using LoRA.
-*   **Training Framework:** Unsloth library for accelerated fine-tuning and merging.
-*   **Task:** Text Generation (specifically, generating structured SOAP notes).
-
-## Paper
-https://arxiv.org/abs/2507.03033
-
-https://www.medrxiv.org/content/10.1101/2025.07.01.25330679v1
-
-## Intended Use
-Input: Free-text medical transcripts (doctor-patient conversations or dictated notes).
-
-Output: Structured medical notes with clearly defined sections (Demographics, Presenting Illness, History, etc.).
-
-
-```python
-
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "OnDeviceMedNotes/Medical_Summary_Notes"  
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
-
-
-SYSTEM_PROMPT = """Convert the following medical transcript to a structured medical note.
-
-Use these sections in this order:
-
-1. Demographics
-   - Name, Age, Sex, DOB
-
-2. Presenting Illness
-   - Bullet point statements of the main problem and duration.
-
-3. History of Presenting Illness
-   - Chronological narrative: symptom onset, progression, modifiers, associated factors.
-
-4. Past Medical History
-   - List chronic illnesses and past medical diagnoses mentioned in the transcript. Do not include surgeries.
-
-5. Surgical History
-   - List prior surgeries with year if known, as mentioned in the transcript.
-
-6. Family History
-   - Relevant family history mentioned in the transcript.
-
-7. Social History
-   - Occupation, tobacco/alcohol/drug use, exercise, living situation if mentioned in the transcript.
-
-8. Allergy History
-   - Drug, food, or environmental allergies and reactions, if mentioned in the transcript.
-
-9. Medication History
-   - List medications the patient is already taking. Do not include any new or proposed drugs in this section.
-
-10. Dietary History
-   - If unrelated, write “Not applicable”; otherwise, summarize the diet pattern.
-
-11. Review of Systems
-    - Head-to-toe, alphabetically ordered bullet points; include both positives and pertinent negatives as mentioned in the transcript.
-
-12. Physical Exam Findings
-    - Vital Signs (BP, HR, RR, Temp, SpO₂, HT, WT, BMI) if mentioned in the transcript.
-    - Structured by system: General, HEENT, Cardiovascular, Respiratory, Abdomen, Neurological, Musculoskeletal, Skin, Psychiatric—as mentioned in the transcript.
-
-13. Labs and Imaging
-    - Summarize labs and imaging results.
-
-14. ASSESSMENT
-    - Provide a brief summary of the clinical assessment or diagnosis based on the information in the transcript.
-
-15. PLAN
-    - Outline the proposed management plan, including treatments, medications, follow-up, and patient instructions as discussed.
-
-Please use only the information present in the transcript. If an information is not mentioned or not applicable, state “Not applicable.” Format each section clearly with its heading.
-"""
-
-def generate_structured_note(transcript):
-    message = [
-        {"role": "system", "content": SYSTEM_PROMPT},
-        {"role": "user", "content": f"<START_TRANSCRIPT>\n{transcript}\n<END_TRANSCRIPT>\n"},
-    ]
-
-    inputs = tokenizer.apply_chat_template(
-        message,
-        tokenize=True,
-        add_generation_prompt=True,
-        return_tensors="pt",
-    ).to(model.device)
-
-    outputs = model.generate(
-        input_ids=inputs,
-        max_new_tokens=2048,
-        temperature=0.2,
-        top_p=0.85,
-        min_p=0.1,
-        top_k=20,
-        do_sample=True,
-        eos_token_id=tokenizer.eos_token_id,
-        use_cache=True,
-    )
-
-    input_token_len = len(inputs[0])
-    generated_tokens = outputs[:, input_token_len:]
-    note = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
-    if "<START_NOTES>" in note:
-       note = note.split("<START_NOTES>")[-1].strip()
-    if "<END_NOTES>" in note:
-       note = note.split("<END_NOTES>")[0].strip()
-    return note
-
-# Example usage
-transcript = "Patient is a 45-year-old male presenting with..."
-note = generate_structured_note(transcript)
-print("\n--- Generated Response ---")
-print(note)
-print("---------------------------")
+---
+datasets:
+- starfishdata/playground_endocronology_notes_1500
+metrics:
+- bertscore
+- bleurt
+- rouge
+library_name: transformers
+base_model:
+- meta-llama/Llama-3.2-1B-Instruct
+license: apache-2.0
+language:
+- en
+---
+
+## Model Details
+*   **Base Model:** [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
+*   **Fine-tuning Method:** PEFT (Parameter-Efficient Fine-Tuning) using LoRA.
+*   **Training Framework:** Unsloth library for accelerated fine-tuning and merging.
+*   **Task:** Text Generation (specifically, generating structured SOAP notes).
+
+## Paper
+https://arxiv.org/abs/2507.03033
+
+https://www.medrxiv.org/content/10.1101/2025.07.01.25330679v1
+
+## Intended Use
+Input: Free-text medical transcripts (doctor-patient conversations or dictated notes).
+
+Output: Structured medical notes with clearly defined sections (Demographics, Presenting Illness, History, etc.).
+
+
+```python
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "GetSoloTech/Llama3.2-Medical-Notes-1B"  
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+
+SYSTEM_PROMPT = """Convert the following medical transcript to a structured medical note.
+
+Use these sections in this order:
+
+1. Demographics
+   - Name, Age, Sex, DOB
+
+2. Presenting Illness
+   - Bullet point statements of the main problem and duration.
+
+3. History of Presenting Illness
+   - Chronological narrative: symptom onset, progression, modifiers, associated factors.
+
+4. Past Medical History
+   - List chronic illnesses and past medical diagnoses mentioned in the transcript. Do not include surgeries.
+
+5. Surgical History
+   - List prior surgeries with year if known, as mentioned in the transcript.
+
+6. Family History
+   - Relevant family history mentioned in the transcript.
+
+7. Social History
+   - Occupation, tobacco/alcohol/drug use, exercise, living situation if mentioned in the transcript.
+
+8. Allergy History
+   - Drug, food, or environmental allergies and reactions, if mentioned in the transcript.
+
+9. Medication History
+   - List medications the patient is already taking. Do not include any new or proposed drugs in this section.
+
+10. Dietary History
+   - If unrelated, write “Not applicable”; otherwise, summarize the diet pattern.
+
+11. Review of Systems
+    - Head-to-toe, alphabetically ordered bullet points; include both positives and pertinent negatives as mentioned in the transcript.
+
+12. Physical Exam Findings
+    - Vital Signs (BP, HR, RR, Temp, SpO₂, HT, WT, BMI) if mentioned in the transcript.
+    - Structured by system: General, HEENT, Cardiovascular, Respiratory, Abdomen, Neurological, Musculoskeletal, Skin, Psychiatric—as mentioned in the transcript.
+
+13. Labs and Imaging
+    - Summarize labs and imaging results.
+
+14. ASSESSMENT
+    - Provide a brief summary of the clinical assessment or diagnosis based on the information in the transcript.
+
+15. PLAN
+    - Outline the proposed management plan, including treatments, medications, follow-up, and patient instructions as discussed.
+
+Please use only the information present in the transcript. If an information is not mentioned or not applicable, state “Not applicable.” Format each section clearly with its heading.
+"""
+
+def generate_structured_note(transcript):
+    message = [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user", "content": f"<START_TRANSCRIPT>\n{transcript}\n<END_TRANSCRIPT>\n"},
+    ]
+
+    inputs = tokenizer.apply_chat_template(
+        message,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_tensors="pt",
+    ).to(model.device)
+
+    outputs = model.generate(
+        input_ids=inputs,
+        max_new_tokens=2048,
+        temperature=0.2,
+        top_p=0.85,
+        min_p=0.1,
+        top_k=20,
+        do_sample=True,
+        eos_token_id=tokenizer.eos_token_id,
+        use_cache=True,
+    )
+
+    input_token_len = len(inputs[0])
+    generated_tokens = outputs[:, input_token_len:]
+    note = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
+    if "<START_NOTES>" in note:
+       note = note.split("<START_NOTES>")[-1].strip()
+    if "<END_NOTES>" in note:
+       note = note.split("<END_NOTES>")[0].strip()
+    return note
+
+# Example usage
+transcript = "Patient is a 45-year-old male presenting with..."
+note = generate_structured_note(transcript)
+print("\n--- Generated Response ---")
+print(note)
+print("---------------------------")
 ```