Model Card for Model ID

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]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

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.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

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

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]