Update README.md

README.md

@@ -1,9 +1,101 @@
 ---
 tags:
+- Code-Generation
 - autotrain
 - text-generation
+- Llama2
+- Pytorch
+- PEFT
+- QLoRA
+- code
+- coding
+pipeline_tag: text-generation
 widget:
-- text: "I love AutoTrain because "
+- text: 'Write a program that add five numbers'
+- text: 'Write a python code for reading multiple images'
+- text: 'Write a python code for the name Ahmed to be in a reversed order'
 ---
 
-# Model Trained Using AutoTrain
+# LlaMa2-CodeGen
+This model is **LlaMa-2 7b** fine-tuned on the **CodeSearchNet dataset instructions dataset** by using the method **QLoRA** with [PEFT](https://github.com/huggingface/peft) library.
+
+# Model Trained on Google Colab Pro Using AutoTrain, PEFT and QLoRA
+
+
+
+
+
+## Llama-2 description 
+
+[Llama-2](https://huggingface.co/meta-llama/Llama-2-7b)
+
+Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters.
+Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
+
+
+
+
+### Example 
+```py
+
+
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+peft_model_id = "AhmedSSoliman/Llama2-CodeGen-PEFT-QLora"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, trust_remote_code=True, return_dict=True, load_in_4bit=True, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+# Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+
+
+def create_prompt(instruction):
+  system = "You are a coding assistant that will help the user to resolve the following instruction:"
+  instruction = "\n### Input: " + instruction
+  return system + "\n" + instruction + "\n\n" + "### Response:" + "\n"
+
+def generate(
+        instruction,
+        max_new_tokens=128,
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=4,
+        **kwargs,
+):
+    prompt = create_prompt(instruction)
+    print(prompt)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].to("cuda")
+    attention_mask = inputs["attention_mask"].to("cuda")
+    generation_config = GenerationConfig(
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        num_beams=num_beams,
+        **kwargs,
+    )
+    with torch.no_grad():
+        generation_output = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            generation_config=generation_config,
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=max_new_tokens,
+            early_stopping=True
+        )
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
+    return output.split("### Response:")[1].lstrip("\n")
+
+
+instruction = """
+ Write a python code for the name Ahmed to be in a reversed order
+"""
+print(generate(instruction))
+```