Update README.md

README.md

@@ -18,10 +18,10 @@ datasets:
 Buffala-LoRA is a 7B-parameter LLaMA model finetuned to follow instructions. It is trained on the Stanford Alpaca (TH), WikiTH, Pantip and IAppQ&A dataset and makes use of the Huggingface LLaMA implementation. For more information, please visit [the project's website](https://github.com/tloen/alpaca-lora).
 
 ## Issues and what next?
-- The model still lacks a significant amount of world knowledge, so it is necessary to fine-tune it on larger Thai datasets.
-- Currently, there is no translation prompt. We plan to fine-tune the model on the SCB Thai-English dataset soon.
-- The model works well with the LangChain Search agent (Serpapi), which serves as a hotfix for world knowledge.
-
+- The model still lacks a significant amount of world knowledge, so it is necessary to fine-tune it on larger Thai datasets > Next version: CCNet,OSCAR,Wiki (TH)
+- Currently, there is no translation prompt. We plan to fine-tune the model on the SCB Thai-English dataset soon. 
+- The model works well with the LangChain Search agent (Serpapi), which serves as a hotfix for world knowledge. > Plan for Spaces with search chain demo
+- Lacked of chat capabilities, waiting for LangChain implementation.
 
 ## How to use
 
@@ -46,6 +46,30 @@ model = PeftModel.from_pretrained(
     torch_dtype=torch.float16,
 )
 
+def generate_prompt(instruction, input=None):
+
+    if input:
+        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+### Instruction:
+{instruction}
+### Input:
+{input + get_list_and_snippet(instruction)}
+### Response:"""
+    else:
+        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Instruction:
+{instruction}
+### Input:
+{get_list_and_snippet(instruction)}
+### Response:"""
+
+if not LOAD_8BIT:
+    model.half()  # seems to fix bugs for some users.
+
+model.eval()
+if torch.__version__ >= "2" and sys.platform != "win32":
+    model = torch.compile(model)
+
 
 def evaluate(
     instruction,