43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299 | class LlamaCPP(CustomLLM):
r"""LlamaCPP LLM.
Examples:
Install llama-cpp-python following instructions:
https://github.com/abetlen/llama-cpp-python
Then `pip install llama-index-llms-llama-cpp`
```python
from llama_index.llms.llama_cpp import LlamaCPP
def messages_to_prompt(messages):
prompt = ""
for message in messages:
if message.role == 'system':
prompt += f"<|system|>\n{message.content}</s>\n"
elif message.role == 'user':
prompt += f"<|user|>\n{message.content}</s>\n"
elif message.role == 'assistant':
prompt += f"<|assistant|>\n{message.content}</s>\n"
# ensure we start with a system prompt, insert blank if needed
if not prompt.startswith("<|system|>\n"):
prompt = "<|system|>\n</s>\n" + prompt
# add final assistant prompt
prompt = prompt + "<|assistant|>\n"
return prompt
def completion_to_prompt(completion):
return f"<|system|>\n</s>\n<|user|>\n{completion}</s>\n<|assistant|>\n"
model_url = "https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF/resolve/main/zephyr-7b-beta.Q4_0.gguf"
llm = LlamaCPP(
model_url=model_url,
model_path=None,
temperature=0.1,
max_new_tokens=256,
context_window=3900,
generate_kwargs={},
model_kwargs={"n_gpu_layers": -1}, # if compiled to use GPU
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
verbose=True,
)
response = llm.complete("Hello, how are you?")
print(str(response))
```
"""
model_url: Optional[str] = Field(
description="The URL llama-cpp model to download and use."
)
model_path: Optional[str] = Field(
description="The path to the llama-cpp model to use."
)
temperature: float = Field(
default=DEFAULT_TEMPERATURE,
description="The temperature to use for sampling.",
ge=0.0,
le=1.0,
)
max_new_tokens: int = Field(
default=DEFAULT_NUM_OUTPUTS,
description="The maximum number of tokens to generate.",
gt=0,
)
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description="The maximum number of context tokens for the model.",
gt=0,
)
generate_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Kwargs used for generation."
)
model_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Kwargs used for model initialization."
)
verbose: bool = Field(
default=DEFAULT_LLAMA_CPP_MODEL_VERBOSITY,
description="Whether to print verbose output.",
)
_model: Any = PrivateAttr()
def __init__(
self,
model_url: Optional[str] = None,
model_path: Optional[str] = None,
temperature: float = DEFAULT_TEMPERATURE,
max_new_tokens: int = DEFAULT_NUM_OUTPUTS,
context_window: int = DEFAULT_CONTEXT_WINDOW,
callback_manager: Optional[CallbackManager] = None,
generate_kwargs: Optional[Dict[str, Any]] = None,
model_kwargs: Optional[Dict[str, Any]] = None,
verbose: bool = DEFAULT_LLAMA_CPP_MODEL_VERBOSITY,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
model_kwargs = {
**{"n_ctx": context_window, "verbose": verbose},
**(model_kwargs or {}), # Override defaults via model_kwargs
}
# check if model is cached
if model_path is not None:
if not os.path.exists(model_path):
raise ValueError(
"Provided model path does not exist. "
"Please check the path or provide a model_url to download."
)
else:
model = Llama(model_path=model_path, **model_kwargs)
else:
cache_dir = get_cache_dir()
model_url = model_url or self._get_model_path_for_version()
model_name = os.path.basename(model_url)
model_path = os.path.join(cache_dir, "models", model_name)
if not os.path.exists(model_path):
os.makedirs(os.path.dirname(model_path), exist_ok=True)
self._download_url(model_url, model_path)
assert os.path.exists(model_path)
model = Llama(model_path=model_path, **model_kwargs)
model_path = model_path
generate_kwargs = generate_kwargs or {}
generate_kwargs.update(
{"temperature": temperature, "max_tokens": max_new_tokens}
)
super().__init__(
model_path=model_path,
model_url=model_url,
temperature=temperature,
context_window=context_window,
max_new_tokens=max_new_tokens,
callback_manager=callback_manager,
generate_kwargs=generate_kwargs,
model_kwargs=model_kwargs,
verbose=verbose,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
self._model = model
@classmethod
def class_name(cls) -> str:
return "LlamaCPP_llm"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self._model.context_params.n_ctx,
num_output=self.max_new_tokens,
model_name=self.model_path,
)
def _get_model_path_for_version(self) -> str:
"""Get model path for the current llama-cpp version."""
import pkg_resources
version = pkg_resources.get_distribution("llama-cpp-python").version
major, minor, patch = version.split(".")
# NOTE: llama-cpp-python<=0.1.78 supports GGML, newer support GGUF
if int(major) <= 0 and int(minor) <= 1 and int(patch) <= 78:
return DEFAULT_LLAMA_CPP_GGML_MODEL
else:
return DEFAULT_LLAMA_CPP_GGUF_MODEL
def _download_url(self, model_url: str, model_path: str) -> None:
completed = False
try:
print("Downloading url", model_url, "to path", model_path)
with requests.get(model_url, stream=True) as r:
with open(model_path, "wb") as file:
total_size = int(r.headers.get("Content-Length") or "0")
if total_size < 1000 * 1000:
raise ValueError(
"Content should be at least 1 MB, but is only",
r.headers.get("Content-Length"),
"bytes",
)
print("total size (MB):", round(total_size / 1000 / 1000, 2))
chunk_size = 1024 * 1024 # 1 MB
for chunk in tqdm(
r.iter_content(chunk_size=chunk_size),
total=int(total_size / chunk_size),
):
file.write(chunk)
completed = True
except Exception as e:
print("Error downloading model:", e)
finally:
if not completed:
print("Download incomplete.", "Removing partially downloaded file.")
os.remove(model_path)
raise ValueError("Download incomplete.")
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
return stream_completion_response_to_chat_response(completion_response)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
self.generate_kwargs.update({"stream": False})
if not formatted:
prompt = self.completion_to_prompt(prompt)
response = self._model(prompt=prompt, **self.generate_kwargs)
return CompletionResponse(text=response["choices"][0]["text"], raw=response)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
self.generate_kwargs.update({"stream": True})
if not formatted:
prompt = self.completion_to_prompt(prompt)
response_iter = self._model(prompt=prompt, **self.generate_kwargs)
def gen() -> CompletionResponseGen:
text = ""
for response in response_iter:
delta = response["choices"][0]["text"]
text += delta
yield CompletionResponse(delta=delta, text=text, raw=response)
return gen()
|
