rwkv.cpp/python/inference_example.py at master · RWKV/rwkv.cpp · GitHub

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# Generates some text with an RWKV model.

# Usage example: python inference_example.py C:\rwkv.cpp-169M-Q5_1.bin 20B

import argparse

import sampling

from rwkv_cpp import rwkv_cpp_shared_library, rwkv_cpp_model

from tokenizer_util import add_tokenizer_argument, get_tokenizer

from typing import List

# Parse received arguments.

parser = argparse.ArgumentParser(description='Generate some text with an RWKV model')

parser.add_argument('model_path', help='Path to RWKV model in ggml format')

parser.add_argument('-ngl', '--num_gpu_layers', type=int, default=99, help='Number of layers to run on GPU')

add_tokenizer_argument(parser)

args = parser.parse_args()

# Load the model.

library = rwkv_cpp_shared_library.load_rwkv_shared_library()

model = rwkv_cpp_model.RWKVModel(library, args.model_path, gpu_layer_count=args.num_gpu_layers)

# Set up the tokenizer.

tokenizer_decode, tokenizer_encode = get_tokenizer(args.tokenizer, model.n_vocab)

# Prepare the prompt.

prompt: str = """One upon a time,"""

prompt_tokens: List[int] = tokenizer_encode(prompt)

# Process the prompt.

logits, state = model.eval_sequence_in_chunks(prompt_tokens, None, None, None, use_numpy=True)

# Generate and print the completion.

print(prompt, end='')

for i in range(32):

token: int = sampling.sample_logits(logits, temperature=0.8, top_p=0.5)

print(tokenizer_decode([token]), end='', flush=True)

logits, state = model.eval(token, state, state, logits, use_numpy=True)

# Don't forget to free the memory after you are done working with the model!

model.free()

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