Update README.md

README.md

@@ -1,20 +1,49 @@
 ---
 library_name: peft
+license: llama2
+datasets:
+- TuningAI/Startups_V2
+language:
+- en
+pipeline_tag: conversational
+tags:
+- law
+- startups
+- finance
+- tax
+- Algerian
 ---
-## Training procedure
 
+## Model Name: **Llama2_13B_startup_Assistant**
 
-The following `bitsandbytes` quantization config was used during training:
-- load_in_8bit: False
-- load_in_4bit: True
-- llm_int8_threshold: 6.0
-- llm_int8_skip_modules: None
-- llm_int8_enable_fp32_cpu_offload: False
-- llm_int8_has_fp16_weight: False
-- bnb_4bit_quant_type: nf4
-- bnb_4bit_use_double_quant: False
-- bnb_4bit_compute_dtype: float16
+## Description:
 
+Llama2_13B_startup_Assistant is a highly specialized language model fine-tuned from Meta's Llama2_13B. 
+It has been tailored to assist with inquiries related to Algerian startups, offering valuable insights and guidance in these domains.
+
+## Base Model: 
+This model is based on the Meta's **meta-llama/Llama-2-13b-chat-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.
+
+## Performance:
+**Llama2_13B_startup_Assistant** exhibits improved performance and efficiency in addressing queries related to Algerian tax law and startups,
+making it a valuable resource for individuals and businesses navigating these areas.
+
+## Limitations:
+
+* While highly specialized, this model may not cover every nuanced aspect of Algerian tax law or the startup ecosystem.
+* Accuracy may vary depending on the complexity and specificity of questions.
+* It may not provide legal advice, and users should seek professional consultation for critical legal matters.
+
+## Training procedure
 The following `bitsandbytes` quantization config was used during training:
 - load_in_8bit: False
 - load_in_4bit: True
@@ -26,7 +55,39 @@ The following `bitsandbytes` quantization config was used during training:
 - bnb_4bit_use_double_quant: False
 - bnb_4bit_compute_dtype: float16
 ### Framework versions
-
 - PEFT 0.4.0
+```
+! huggingface-cli login
+```
+```python
+from transformers import pipeline
+from transformers import AutoTokenizer
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM , BitsAndBytesConfig
+import torch
 
-- PEFT 0.4.0
+#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"
+Instruction = "Given a user's information about the target job, you will generate a Cover letter for this job based on this information."
+while 1:
+  input_text = input(">>>")
+  logging.set_verbosity(logging.CRITICAL)
+  prompt = f"### Instruction\n{Instruction}.\n ###Input \n\n{input_text}. ### Output:"
+  pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer,max_length=400)
+  result = pipe(prompt)
+  print(result[0]['generated_text'].replace(prompt, ''))
+```