Qwen2.5-14B-Instruct-SOAP-tuned-Q8

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

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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

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from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("{HF_USERNAME}/{model_name}")
model = AutoModelForCausalLM.from_pretrained(
    "yehiazak/Qwen2.5-14B-Instruct-SOAP-tuned-Q8",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Generate text
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512, temperature=0.2)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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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

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Training Details

Training Data

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Dataset (total samples: 1473) was shuffled and slplit into:

• Training samples: 1300
• Validation samples: 173

Training Procedure

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LoRA fine-tuning with 3 epochs.

Preprocessing [optional]

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Training Hyperparameters

• Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

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Evaluation

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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

=================== 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

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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