delete test file (#12)

- delete test file (85011d87470197f5712603fe27484293d54e6047)


Co-authored-by: Abdalgader Abubaker <abdalgader@users.noreply.huggingface.co>

test.py

@@ -1,93 +0,0 @@
-import os
-import random
-
-# Set environment variables FIRST, before any other imports
-os.environ["PYTHONHASHSEED"] = "42"
-os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-from pathlib import Path
-
-import numpy as np
-import torch
-from tqdm import tqdm
-from transformers import AutoTokenizer
-from transformers import set_seed as transformers_set_seed
-from vllm import LLM, SamplingParams
-
-current_dir = Path(__file__).parent
-
-
-def set_seed(seed: int = 42):
-    """Set seed for reproducibility across all libraries"""
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(seed)
-        torch.cuda.manual_seed_all(seed)
-
-    transformers_set_seed(seed)
-    torch.use_deterministic_algorithms(True)
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = False
-
-
-def main() -> None:
-    set_seed(41)
-    path = "../Falcon-H1R-7B"
-    tokenizer = AutoTokenizer.from_pretrained(path)
-    samples = [
-        "hi",
-        "what is 1+1?",
-        "what is the capital of france?",
-        "who are you?",
-        "solve 3x+1=0",
-        "Write a python function that returns the factorial of a number using recursion.",
-        "A train leaves Station A at 9:00 AM traveling at 60 mph toward Station B. Another train leaves Station B at 10:00 AM traveling at 80 mph toward Station A. If the stations are 280 miles apart, at what time do the trains meet?",
-        "Find the sum of all integer bases $b>9$ for which $17_b$ is a divisor of $97_b.$",
-    ]
-
-    # prepare Vllm input
-    inputs = [
-        tokenizer.apply_chat_template(
-            [{"role": "user", "content": sample}],
-            tokenize=False,
-            add_generation_prompt=True,
-        )
-        # + "<think>\n</think>\n"
-        for sample in tqdm(samples)
-    ]
-
-    # Load model in vllm
-    llm_kwargs = {
-        "model": path,
-        "trust_remote_code": True,
-        "tensor_parallel_size": 2,
-    }
-    llm = LLM(**llm_kwargs)
-
-    # Sampling params
-    gen_kwargs = {
-        "max_tokens": 32768,
-        "temperature": 0.6,
-        "top_p": 0.95,
-        "stop": ["<|endoftext|>", "<|im_end|>", "</s>", "<|eot_id|>", "<|end|>"],
-    }
-    sampling_params = SamplingParams(**gen_kwargs)
-
-    # generate
-    outputs = llm.generate(inputs, sampling_params)
-    generated_texts = [output.outputs[0].text for output in outputs]
-
-    # display
-    for sample, generated_text in zip(samples, generated_texts):
-        print("User:")
-        print(sample)
-        print("Assisant:")
-        print(generated_text)
-        print("-" * 50)
-
-
-main()