Upload colab_script.py

colab_script.py

@@ -0,0 +1,89 @@
+# Code adapted from https://github.com/tloen/alpaca-lora
+!pip install git+https://github.com/huggingface/transformers.git
+!pip install sentencepiece
+!pip install peft
+!pip install accelerate
+!pip install bitsandbytes
+import torch
+from peft import PeftModel
+import transformers
+
+from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
+
+tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print(device)
+model = LlamaForCausalLM.from_pretrained(
+    "decapoda-research/llama-7b-hf",
+    load_in_8bit=True,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    offload_folder = "."
+)
+
+model = PeftModel.from_pretrained(
+    model, 
+    "tloen/alpaca-lora-7b",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    load_in_8bit = True,
+    offload_folder="."
+)
+
+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}
+
+### Response:"""
+    else:
+        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+### Response:"""
+
+
+model.eval()
+
+
+def evaluate(
+        instruction,
+        input=None,
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=4,
+        **kwargs,
+):
+    prompt = generate_prompt(instruction, input)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].to(device)
+    generation_config = GenerationConfig(
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        num_beams=num_beams,
+        **kwargs,
+    )
+    with torch.no_grad():
+        generation_output = model.generate(
+            input_ids=input_ids,
+            generation_config=generation_config,
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=2048,
+        )
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
+    return output.split("### Response:")[1].strip()
+input = """RESEARCH REPORT NO. 1037027Final Clinical Study Report -NV25118: A Randomized, Multicentre, Single Blinded, Parallel Study of the Safety of 100 mg and 200 mg Oseltamivir Administered Intravenously for the Treatment of Influenza in Patients Aged > 13 Years. Report No. 1037027. June 3, 2013 Study Sponsor(s):Dr F Hoffman La-Roche LtdStudy Dates:First patient screened: Jan 15, 2010 Last patient visit: Sept 14, 2012Trial Phase:II/IIIIndication:InfluenzaName of Principal Investigator Thomas MathewSponsor's Signatory: Personnel Responsible for Clinical and Statistical Analyses: Clinical analysis:John ThomasSafety analysis:William RichardStatistical analysis:Charles DanielPharmacokinetic analysis:Robert DavidGCP Compliance: This study was conducted in accordance with the principles of GCP"""
+instruction = "Extract Names, Diseases, Dates from below text:"
+# input = "RESEARCH REPORT NO. 1037027Final Clinical Study Report -NV25118: A Randomized, Multicentre, Single Blinded, Parallel Study of the Safety of 100 mg and 200 mg Oseltamivir Administered Intravenously for the Treatment of Influenza in Patients Aged > 13 Years. Report No. 1037027. June 3, 2013 Study Sponsor(s):Dr F Hoffman La-Roche LtdStudy Dates:First patient screened: Jan 15, 2010 Last patient visit: Sept 14, 2012"
+print("Response:", evaluate(instruction,input))