Update README.md

README.md

@@ -1,5 +1,97 @@
 ---
-license: other
-license_name: gama-platform
-license_link: LICENSE
+library_name: transformers
+base_model: mistralai/Mistral-7B-v0.1
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- code
 ---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+> This finetuned model is already merged with Mistral7B (Base model)
+> There will be 2 options running this model for inference 
+> - _Option 1:_ Load base model and use **Peft library** to load parameters of finetuned model on base model
+> - _Option 2:_ Load finetuned model straight from this huggingface hub
+
+## Approach 1
+### Run Inference on Google Colab
+1. First run this code to load the base model which is Mistral-7B-v0.1
+```py
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+base_model_id = "mistralai/Mistral-7B-v0.1"
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,  # Mistral, same as before
+    quantization_config=bnb_config,  # Same quantization config as before
+    device_map="auto",
+    trust_remote_code=True,
+    use_auth_token=True
+)
+
+eval_tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token=True, trust_remote_code=True)
+
+```
+2. After that, we would use Peft library to merge the new parameters that we already finetuned with GAML with this code
+```py
+from peft import PeftModel
+import torch
+# ft_model = PeftModel.from_pretrained(base_model, "mistral-gama-finetune_allblocks_newdata/checkpoint-45")
+ft_model = PeftModel.from_pretrained(base_model, "Phanh2532/GAML-151-500")
+#ft_model3 = PeftModel.from_pretrained(base_model, "mistral-allbloclks//checkpoint-250")
+#ft_model.save_pretrained('/content/mistral-allblocksft/')
+eval_prompt = "Create a GAML code snippet inspired by water pollution in real life"
+model_input = eval_tokenizer(eval_prompt, return_tensors="pt").to("cuda")
+ft_model.eval()
+with torch.no_grad():
+    print(eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=2000, repetition_penalty=1.15)[0], skip_special_tokens=True))
+    print('----------------------------------------------------------------------')
+    #print(eval_tokenizer.decode(ft_model2.generate(**model_input, max_new_tokens=2000, repetition_penalty=1.15)[0], skip_special_tokens=True))
+```
+
+## Approach 2
+Run this code snippet
+```py
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+# Load Mistral 7B model and tokenizer
+model_id = "Phanh2532/GAML-151-500"
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True,
+    use_auth_token=True
+)
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id, add_bos_token=True, trust_remote_code=True)
+with torch.no_grad():
+    print(tokenizer.decode(model.generate(**model_input, max_new_tokens=2000, repetition_penalty=1.15)[0], skip_special_tokens=True))
+    print('----------------------------------------------------------------------')
+    # print(eval_tokenizer.decode(ft_model2.generate(**model_input, max_new_tokens=2000, repetition_penalty=1.15)[0], skip_special_tokens=True))
+```
+
+
+### Framework versions
+
+- PEFT 0.7.2.dev0