Model Card for Model ID
This model is a LoRA fine-tuned version of base model Qwen/Qwen2.5-14B-Instruct to improve the SOAP (Subjective, Objective, Assessment, Plan) notes generation from an input doctor-patient dialog. This is an 8-bit quantized version.
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: [Yehia Zakaria]
- Finetuned from model [Qwen/Qwen2.5-14B-Instruct]:
Model Sources [optional]
Uses
1from transformers import AutoTokenizer, AutoModelForCausalLM
2import torch
3
4# Load model and tokenizer
5tokenizer = AutoTokenizer.from_pretrained("{HF_USERNAME}/{model_name}")
6model = AutoModelForCausalLM.from_pretrained(
7 "yehiazak/Qwen2.5-14B-Instruct-SOAP-tuned-Q8",
8 torch_dtype=torch.bfloat16,
9 device_map="auto"
10)
11
12# Generate text
13prompt = "Your prompt here"
14inputs = tokenizer(prompt, return_tensors="pt")
15outputs = model.generate(**inputs, max_length=512, temperature=0.2)
16response = tokenizer.decode(outputs[0], skip_special_tokens=True)
17print(response)
Direct Use
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
Dataset (total samples: 1473) was shuffled and slplit into:
- Training samples: 1300
- Validation samples: 173
Training Procedure
LoRA fine-tuning with 3 epochs.
Preprocessing [optional]
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Training Hyperparameters
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
BertScore computed using "microsoft/deberta-xlarge-mnli".
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Results
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EVALUATION RESULTS
ROUGE-1: 0.7017
ROUGE-2: 0.4914
ROUGE-L: 0.6132
BertScore Precision: 0.8494
BertScore Recall: 0.8288
BertScore F1: 0.8382
Summary
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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