---
library_name: transformers
language:
- ar
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->




### Direct Use

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("health360/Sehty360-llama-3-8b-arabic-health-instruct")
model = AutoModelForCausalLM.from_pretrained("health360/Sehty360-llama-3-8b-arabic-health-instruct", device_map='auto', torch_dtype=torch.bfloat16)

text = """
### Input:
سلام عليكم اشعر بضيق في التنفس واعاني من كثرة البلغم
### Response:
"""
stop_word = "###END###"

# Encode the input text
inputs = tokenizer(text, return_tensors='pt').to('cuda:0')

# Remove token type ids if present, not all models use them
inputs.pop("token_type_ids", None)

# Generating outputs with stopping criteria
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    do_sample=False,
    early_stopping=True,
    temperature=0.8,
    top_k=50,
    top_p=0.95,
    num_return_sequences=1,
    eos_token_id=tokenizer.encode(stop_word, add_special_tokens=False)[0]  # Set EOS token to your stop word
)
outputs = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(outputs)

### Input:
سلام عليكم اشعر بضيق في التنفس واعاني من كثرة البلغم
### Response:
وعليكم السلام! أنا هنا لمساعدتك. ضيق التنفس مع وجود بلغم يمكن أن يكون مؤشراً على وجود عدوى في الرئة أو القصبات.

أوصي بأن تقوم بزيارة طبيب مختص بأمراض الرئة والصدرية للحصول على تشخيص دقيق. يمكن للطبيب أن يطلب إجراء فحوصات دم، أشعة على الصدر، أو حتى اختبارات أخرى مثل تخطيط الرئة  لتحديد نوع العدوى والمضاد المناسب لها.

إذا كنت ترغب، يمكنني مساعدتك في العثور على طبيب مختص بأمراض الرئة والصدرية في منطقتك. هل تود معرفة معلومات عن الأطباء المتاحين في منطقتك؟

### 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

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Hardware

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## Citation [optional]

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## Glossary [optional]

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