NeuraOrcaGemma7b

README.md

@@ -1,14 +1,21 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+language:
+- fa
+pipeline_tag: text-generation
+tags:
+- orca
+- persian_orca
+- neura
+datasets:
+- microsoft/orca-math-word-problems-200k
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-
-
 ## Model Details
 
 ### Model Description
@@ -17,185 +24,106 @@ tags: []
 
 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:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** Neura company
+- **Funded by [optional]:** Neura
+- **Model type:** gemma7b
+- **Language(s) (NLP):** Persian
+- **Finetuned from model [optional]:** google/gemma-7b-it
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
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-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://huggingface.co/google/gemma-7b-it
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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.
-
-[More Information Needed]
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-## Training Details
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-### Training Data
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-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure 
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+Check out also the Google Colab demo to run NeuraOrcaGemma7b 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/NeuraGemma"
+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>
+```
 
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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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-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-[More Information Needed]
-
-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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 ## More Information [optional]
-
-[More Information Needed]
+https://neura.info
 
 ## Model Card Authors [optional]
-
-[More Information Needed]
+Esmaeil Zahedi, Mohsen Yazdinejad
 
 ## Model Card Contact
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-[More Information Needed]
-
-
+[email protected]