mirror of
https://github.com/tinygrad/tinygrad.git
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f7d4638e05
* start LLM app, tons of clean up required. target is 200 line ollama * kind of works * simpler * add k/v cache * with SYM=1, it loops * no rope cache * simpler * more cleanups * cleanups * works * argparse and comments * from gguf * generate is a function * no copy from cpu * fix max context pass in * test * improve test * ai2_arc * fix 8B, use less ram * 136 lines
36 lines
1.6 KiB
Python
36 lines
1.6 KiB
Python
# eval for tinygrad.apps.llm
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import pyarrow.parquet as pq
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from tinygrad.helpers import fetch, colored
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from tinygrad.apps.llm import Transformer, SimpleTokenizer, models
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from tinygrad import Tensor
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if __name__ == "__main__":
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dat = fetch("https://huggingface.co/datasets/allenai/ai2_arc/resolve/main/ARC-Challenge/test-00000-of-00001.parquet")
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table = pq.read_table(dat)
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model, kv = Transformer.from_gguf(Tensor.from_url(models["1B"]), max_context=4096)
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tok = SimpleTokenizer(kv["tokenizer.ggml.tokens"])
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bos_id: int = kv['tokenizer.ggml.bos_token_id']
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eos_id: int = kv['tokenizer.ggml.eos_token_id']
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num_correct, num_answered = 0, 0
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total_questions = len(table["question"])
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for question, choices, answer in zip(table["question"], table["choices"], table["answerKey"]):
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phrasing = f"Question: {question}\n\n" + \
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'\n'.join([f"{k}) {v}" for k,v in zip(choices['label'], choices['text'])]) +\
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"\n\nReply with the letter of the correct answer only."
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try:
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ids = [bos_id] + tok.role("user") + tok.encode(phrasing) + [eos_id] + tok.role("assistant") + tok.encode("Answer: ")
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except RuntimeError:
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# TODO: fix the tokenizer
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pass
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next_id = next(model.generate(ids))
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correct, given = answer.as_py().strip(), tok.decode([next_id]).strip()
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num_correct += correct == given
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num_answered += 1
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print(f"{num_answered:4d}/{total_questions:4d} "+\
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f"Correct Answer: {correct} "+\
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f"Given Answer: {colored(given, 'green' if correct==given else 'red')} "+\
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f"Percent: {num_correct*100.0/num_answered:.2f}%")
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