mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-04 09:32:00 +00:00
cfa0750bc9
* add interface for float input * fixed inpL shape and type * add examples of input floats * add test example for embd input * fixed sampling * add free for context * fixed add end condition for generating * add examples for llava.py * add READMD for llava.py * add READMD for llava.py * add example of PandaGPT * refactor the interface and fixed the styles * add cmake build for embd-input * add cmake build for embd-input * Add MiniGPT-4 example * change the order of the args of llama_eval_internal * fix ci error
72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
import ctypes
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from ctypes import cdll, c_char_p, c_void_p, POINTER, c_float, c_int
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import numpy as np
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import os
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libc = cdll.LoadLibrary("./libembdinput.so")
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libc.sampling.restype=c_char_p
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libc.create_mymodel.restype=c_void_p
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libc.eval_string.argtypes=[c_void_p, c_char_p]
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libc.sampling.argtypes=[c_void_p]
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libc.eval_float.argtypes=[c_void_p, POINTER(c_float), c_int]
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class MyModel:
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def __init__(self, args):
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argc = len(args)
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c_str = [c_char_p(i.encode()) for i in args]
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args_c = (c_char_p * argc)(*c_str)
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self.model = c_void_p(libc.create_mymodel(argc, args_c))
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self.max_tgt_len = 512
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self.print_string_eval = True
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def __del__(self):
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libc.free_mymodel(self.model)
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def eval_float(self, x):
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libc.eval_float(self.model, x.astype(np.float32).ctypes.data_as(POINTER(c_float)), x.shape[1])
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def eval_string(self, x):
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libc.eval_string(self.model, x.encode()) # c_char_p(x.encode()))
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if self.print_string_eval:
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print(x)
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def eval_token(self, x):
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libc.eval_id(self.model, x)
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def sampling(self):
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s = libc.sampling(self.model)
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return s
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def stream_generate(self, end="</s>"):
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ret = b""
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end = end.encode()
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for _ in range(self.max_tgt_len):
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tmp = self.sampling()
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ret += tmp
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yield tmp
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if ret.endswith(end):
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break
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def generate_with_print(self, end="</s>"):
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ret = b""
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for i in self.stream_generate(end=end):
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ret += i
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print(i.decode(errors="replace"), end="", flush=True)
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print("")
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return ret.decode(errors="replace")
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def generate(self, end="</s>"):
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text = b"".join(self.stream_generate(end=end))
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return text.decode(errors="replace")
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if __name__ == "__main__":
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model = MyModel(["main", "--model", "../llama.cpp/models/ggml-vic13b-q4_1.bin", "-c", "2048"])
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model.eval_string("""user: what is the color of the flag of UN?""")
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x = np.random.random((5120,10))# , dtype=np.float32)
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model.eval_float(x)
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model.eval_string("""assistant:""")
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for i in model.generate():
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print(i.decode(errors="replace"), end="", flush=True)
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