Create README.md

README.md

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+---
+datasets:
+- yahma/alpaca-cleaned
+- Nebulous/gpt4all_pruned
+language:
+- en
+---
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+peft_model_id = "edu-linguistic/opt-1.3b-edu-sft"
+model_name = 'facebook/opt-1.3b'
+
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+model = PeftModel.from_pretrained(model, peft_model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+question = "<|prompter|> Consider the following function: f(x1, x2) = ln(x1). This function is…"
+
+question = tokenizer.encode(question, return_tensors='pt')
+
+generation_kwargs = {
+    "do_sample": True,
+    "top_k": 0,
+    "top_p": 0.9,
+    "bos_token_id": tokenizer.bos_token_id,
+    "pad_token_id": tokenizer.pad_token_id,
+    "eos_token_id": tokenizer.eos_token_id,
+    "num_return_sequences": 1,
+    "min_new_tokens": 10,
+    "max_new_tokens": 512,
+}
+
+response = model.generate(input_ids=question, **generation_kwargs)
+response = tokenizer.decode(response[0],
+                           skip_special_tokens=False,
+                           clean_up_tokenization_spaces=False
+                           )
+print(response)