Neurai
/

NeuraChatLlama3_8b

@@ -1,199 +1,128 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+license: apache-2.0
+language:
+- fa
+pipeline_tag: text-generation
+tags:
+- llama3
+- persian_llama
+- neura
 ---
+# Neura Chat llama3 7B
+<p align="center">
+  <img src="neura_gemma.png" width=512 height=256 />
+</p>
+<!-- Provide a quick summary of what the model is/does. -->
+## Model Description
 <!-- Provide a longer summary of what this model is. -->
+- **Developed by:** Neura company
+- **Funded by:** Neura
+- **Model type:** llama3
+- **Language(s) (NLP):** Persian
+- **Finetuned from model:** meta-llama/Meta-Llama-3-8B-Instruct
+### Model Sources
 <!-- Provide the basic links for the model. -->
+- **Repository:** https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
 ## Uses
+Check out the Google Colab demo to run NeuraChatLlama3_7b on a free-tier Google Colab instance: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wgiHZIi199MyI4YT3ZBiMj9je2IoA4Jd?usp=sharing)
+make sure these packages are installed:
+```
+!pip install --no-deps xformers accelerate bitsandbytes
+!pip install -q -U transformers
+```
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+import os
+MODEL_PATH = "Neurai/NeuraOrcaGemma7b"
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit = True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    device_map = "auto",
+    trust_remote_code = True,
+    quantization_config=quantization_config,
+)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+input_prompt = """
+### Instruction:{}
+### Input:{}
+### Response:{}"""
+input_text = input_prompt.format(
+        "در مورد سری فوریه بهم توضیح میدی و فرمولش رو برام مینویسی",
+        "",
+        ""
+    )
+inputs = tokenizer([input_text], return_tensors = "pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens = 300, use_cache = True)
+response = tokenizer.batch_decode(outputs)[0]
+```
+Generated text :
+```
+ سری فوریه یک سری ریاضی است که برای نمایش یک تابع پیوسته و متناوب در یک بازه زمانی معین استفاده می شود. این نام از ریاضیدان فرانسوی آگوستین-لویی کوشی گرفته شده است که آن را در سال 1826 معرفی کرد.
+فرمول سری فوریه به صورت زیر است:
+f(x) = a0 + sum(an * cos(n*pi*x/L)) + sum(bn * sin(n*pi*x/L))
+که در آن:
+- f(x) تابع پیوسته و متناوب در بازه زمانی [(-L/2, L/2] است
+- a0 مقدار میانگین مربع f(x) در بازه زمانی [(-L/2, L/2] است
+- an ضریب سری برای n-th term است
+- bn ضریب سری برای n-th term است
+- L طول بازه زمانی است
+- n یک عدد صحیح مثبت است
+- x یک متغیر واقعی است
+- pi (pi) یک ثابت ریاضی است
+برای یافتن ضرایب سری، باید f(x) را در بازه زمانی [(-L/2, L/2] با استفاده از فرمول های زیر تجزیه کنیم:
+an = (1/L) * int(-L/2, L/2) f(x) * cos(n*pi*x/L) dx
+bn = (1/L) * int(-L/2, L/2) f(x) * sin(n*pi*x/L) dx
+که در آن:
+- int نشان دهنده انتگرال است
+- L طول بازه زمانی است
+- n یک عدد صحیح مثبت است
+- x یک متغیر واقعی است
+- f(x) تابع پیوسته و متناوب در بازه زمانی [(-L/2, L/2] است
+- pi (pi) یک ثابت ریاضی است
+پس از یافتن ضرایب سری، می توان از فرمول سری فوریه برای نمایش f(x) در بازه زمانی [(-L/2, L/2] استفاده کرد.<eos>
+```
+## More Information
+https://neura.info
+## Model Card Authors
+Esmaeil Zahedi, Mohsen Yazdinejad
 ## Model Card Contact
+[email protected]