Update README.md

README.md

@@ -1,6 +1,44 @@
 ---
 library_name: peft
+license: llama2
+datasets:
+- TuningAI/Cover_letter_v2
+language:
+- en
+pipeline_tag: text-generation
 ---
+## Model Name: **Llama2_7B_Cover_letter_generator**
+## Description:
+**Llama2_7B_Cover_letter_generator** is a powerful, custom language model that has been meticulously fine-tuned to excel at generating cover letters for various job positions. 
+It serves as an invaluable tool for automating the creation of personalized cover letters, tailored to specific job descriptions.
+## Base Model: 
+This model is based on the Meta's "meta-llama/Llama-2-7b-hf" architecture, making it a highly capable foundation for generating human-like text responses.
+
+## Dataset :
+This model was fine-tuned on a custom dataset meticulously curated with more than 200 unique examples.
+The dataset incorporates both manual entries and contributions from GPT3.5, GPT4, and Falcon 180B models.
+
+## Fine-tuning Techniques:
+Fine-tuning was performed using QLoRA (Quantized LoRA), an extension of LoRA that introduces quantization for enhanced parameter efficiency.
+The model benefits from 4-bit NormalFloat (NF4) quantization and Double Quantization techniques, ensuring optimized performance.
+
+## Use Cases:
+
+* **Automating Cover Letter Creation:** Llama2_7B_Cover_letter_generator can be used to rapidly generate cover letters for a wide range of job openings, saving time and effort for job seekers.
+
+## Performance:
+
+* Llama2_7B_Cover_letter_generator exhibits impressive performance in generating context-aware cover letters with high coherence and relevance to job descriptions.
+* It maintains a low perplexity score, indicating its ability to generate text that aligns well with user input and desired contexts.
+* The model's quantization techniques enhance its efficiency without significantly compromising performance.
+
+## Limitations:
+
+While the model excels in generating cover letters, it may occasionally produce text that requires minor post-processing for perfection.
++ It may not fully capture highly specific or niche job requirements, and some manual customization might be necessary for certain applications.
++ Llama2_7B_Cover_letter_generator's performance may vary depending on the complexity and uniqueness of the input prompts.
++ Users should be mindful of potential biases in the generated content and perform appropriate reviews to ensure inclusivity and fairness.
+
 ## Training procedure
 
 
@@ -18,3 +56,42 @@ The following `bitsandbytes` quantization config was used during training:
 
 
 - PEFT 0.4.0
+
+
+## How to Get Started with the Model
+
+```
+! huggingface-cli login
+```
+
+```python
+from transformers import pipeline
+from transformers import AutoTokenizer
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM , BitsAndBytesConfig
+import torch
+
+#config = PeftConfig.from_pretrained("ayoubkirouane/Llama2_13B_startup_hf")
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=getattr(torch, "float16"),
+    bnb_4bit_use_double_quant=False)
+model = AutoModelForCausalLM.from_pretrained(
+        "meta-llama/Llama-2-7b-hf",
+        quantization_config=bnb_config,
+        device_map={"": 0})
+model.config.use_cache = False
+model.config.pretraining_tp = 1
+model = PeftModel.from_pretrained(model, "TuningAI/Llama2_7B_Cover_letter_generator")
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf" , trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+while 1:
+  input_text = input(">>>")
+  logging.set_verbosity(logging.CRITICAL)
+  prompt = f"### Instruction\n{system_message}.\n ###Input \n\n{input_text}. ### Output:"
+  pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer,max_length=512)
+  result = pipe(prompt)
+  print(result[0]['generated_text'].replace(prompt, ''))
+```