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fix chat-style prompt templates (#1970)

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Also use a new version of Mistral OpenOrca.

Signed-off-by: Jared Van Bortel <jared@nomic.ai>
This commit is contained in:
Jared Van Bortel
2024-02-21 15:45:32 -05:00
committed by GitHub
parent b8f5c74f40
commit 4fc4d94be4
22 changed files with 429 additions and 307 deletions
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84
gpt4all-bindings/python/docs/gpt4all_python.md
View File

@@ -246,90 +246,6 @@ To do the same outside a session, the input has to be formatted manually. For ex
The colors in my previous response are blue, green and red.
```
Ultimately, the method `GPT4All._format_chat_prompt_template()` is responsible for formatting templates. It can be
customized in a subclass. As an example:
=== "Custom Subclass"
``` py
from itertools import cycle
from gpt4all import GPT4All
class RotatingTemplateGPT4All(GPT4All):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._templates = [
"Respond like a pirate.",
"Respond like a politician.",
"Respond like a philosopher.",
"Respond like a Klingon.",
]
self._cycling_templates = cycle(self._templates)
def _format_chat_prompt_template(
self,
messages: list,
default_prompt_header: str = "",
default_prompt_footer: str = "",
) -> str:
full_prompt = default_prompt_header + "\n\n" if default_prompt_header != "" else ""
for message in messages:
if message["role"] == "user":
user_message = f"USER: {message['content']} {next(self._cycling_templates)}\n"
full_prompt += user_message
if message["role"] == "assistant":
assistant_message = f"ASSISTANT: {message['content']}\n"
full_prompt += assistant_message
full_prompt += "\n\n" + default_prompt_footer if default_prompt_footer != "" else ""
print(full_prompt)
return full_prompt
```
=== "GPT4All Custom Subclass Example"
``` py
model = RotatingTemplateGPT4All('wizardlm-13b-v1.2.Q4_0.gguf')
with model.chat_session(): # starting a session is optional in this example
response1 = model.generate("hi, who are you?")
print(response1)
print()
response2 = model.generate("what can you tell me about snakes?")
print(response2)
print()
response3 = model.generate("what's your opinion on Chess?")
print(response3)
print()
response4 = model.generate("tell me about ancient Rome.")
print(response4)
```
=== "Possible Output"
```
USER: hi, who are you? Respond like a pirate.
Pirate: Ahoy there mateys! I be Cap'n Jack Sparrow of the Black Pearl.
USER: what can you tell me about snakes? Respond like a politician.
Politician: Snakes have been making headlines lately due to their ability to
slither into tight spaces and evade capture, much like myself during my last
election campaign. However, I believe that with proper education and
understanding of these creatures, we can work together towards creating a
safer environment for both humans and snakes alike.
USER: what's your opinion on Chess? Respond like a philosopher.
Philosopher: The game of chess is often used as an analogy to illustrate the
complexities of life and decision-making processes. However, I believe that it
can also be seen as a reflection of our own consciousness and subconscious mind.
Just as each piece on the board has its unique role to play in shaping the
outcome of the game, we too have different roles to fulfill in creating our own
personal narrative.
USER: tell me about ancient Rome. Respond like a Klingon.
Klingon: Ancient Rome was once a great empire that ruled over much of Europe and
the Mediterranean region. However, just as the Empire fell due to internal strife
and external threats, so too did my own house come crashing down when I failed to
protect our homeworld from invading forces.
```
### Introspection
A less apparent feature is the capacity to log the final prompt that gets sent to the model. It relies on

19
gpt4all-bindings/python/gpt4all/_pyllmodel.py
View File

@@ -89,10 +89,12 @@ RecalculateCallback = ctypes.CFUNCTYPE(ctypes.c_bool, ctypes.c_bool)
llmodel.llmodel_prompt.argtypes = [
ctypes.c_void_p,
ctypes.c_char_p,
ctypes.c_char_p,
PromptCallback,
ResponseCallback,
RecalculateCallback,
ctypes.POINTER(LLModelPromptContext),
ctypes.c_bool,
]
llmodel.llmodel_prompt.restype = None
@@ -290,6 +292,7 @@ class LLModel:
def prompt_model(
self,
prompt: str,
prompt_template: str,
callback: ResponseCallbackType,
n_predict: int = 4096,
top_k: int = 40,
@@ -300,6 +303,7 @@ class LLModel:
repeat_last_n: int = 10,
context_erase: float = 0.75,
reset_context: bool = False,
special: bool = False,
):
"""
Generate response from model from a prompt.
@@ -326,9 +330,6 @@ class LLModel:
prompt,
)
prompt_bytes = prompt.encode()
prompt_ptr = ctypes.c_char_p(prompt_bytes)
self._set_context(
n_predict=n_predict,
top_k=top_k,
@@ -343,16 +344,18 @@ class LLModel:
llmodel.llmodel_prompt(
self.model,
prompt_ptr,
ctypes.c_char_p(prompt.encode()),
ctypes.c_char_p(prompt_template.encode()),
PromptCallback(self._prompt_callback),
ResponseCallback(self._callback_decoder(callback)),
RecalculateCallback(self._recalculate_callback),
self.context,
special,
)
def prompt_model_streaming(
self, prompt: str, callback: ResponseCallbackType = empty_response_callback, **kwargs
self, prompt: str, prompt_template: str, callback: ResponseCallbackType = empty_response_callback, **kwargs
) -> Iterable[str]:
output_queue: Queue[str | Sentinel] = Queue()
@@ -369,15 +372,15 @@ class LLModel:
return _generator_callback
def run_llmodel_prompt(prompt: str, callback: ResponseCallbackType, **kwargs):
self.prompt_model(prompt, callback, **kwargs)
def run_llmodel_prompt(prompt: str, prompt_template: str, callback: ResponseCallbackType, **kwargs):
self.prompt_model(prompt, prompt_template, callback, **kwargs)
output_queue.put(Sentinel.TERMINATING_SYMBOL)
# Kick off llmodel_prompt in separate thread so we can return generator
# immediately
thread = threading.Thread(
target=run_llmodel_prompt,
args=(prompt, _generator_callback_wrapper(callback)),
args=(prompt, prompt_template, _generator_callback_wrapper(callback)),
kwargs=kwargs,
)
thread.start()

61
gpt4all-bindings/python/gpt4all/gpt4all.py
View File

@@ -4,8 +4,10 @@ Python only API for running all GPT4All models.
from __future__ import annotations
import os
import re
import sys
import time
import warnings
from contextlib import contextmanager
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional, Union
@@ -314,6 +316,10 @@ class GPT4All:
Either the entire completion or a generator that yields the completion token by token.
"""
if re.search(r"%1(?![0-9])", self._current_prompt_template):
raise ValueError("Prompt template containing a literal '%1' is not supported. For a prompt "
"placeholder, please use '{0}' instead.")
# Preparing the model request
generate_kwargs: Dict[str, Any] = dict(
temp=temp,
@@ -327,16 +333,29 @@ class GPT4All:
if self._is_chat_session_activated:
# check if there is only one message, i.e. system prompt:
generate_kwargs["reset_context"] = len(self.current_chat_session) == 1
reset = len(self.current_chat_session) == 1
generate_kwargs["reset_context"] = reset
self.current_chat_session.append({"role": "user", "content": prompt})
prompt = self._format_chat_prompt_template(
messages=self.current_chat_session[-1:],
default_prompt_header=self.current_chat_session[0]["content"]
if generate_kwargs["reset_context"]
else "",
)
if self._format_chat_prompt_template.__func__ is GPT4All._format_chat_prompt_template:
if reset:
# ingest system prompt
self.model.prompt_model(self.current_chat_session[0]["content"], "%1",
n_batch=n_batch, n_predict=0, special=True)
prompt_template = self._current_prompt_template.format("%1")
else:
warnings.warn(
"_format_chat_prompt_template is deprecated. Please use a chat session with a prompt template.",
DeprecationWarning,
)
# special tokens won't be processed
prompt = self._format_chat_prompt_template(
self.current_chat_session[-1:],
self.current_chat_session[0]["content"] if reset else "",
)
prompt_template = "%1"
else:
prompt_template = "%1"
generate_kwargs["reset_context"] = True
# Prepare the callback, process the model response
@@ -365,14 +384,16 @@ class GPT4All:
# Send the request to the model
if streaming:
return self.model.prompt_model_streaming(
prompt=prompt,
callback=_callback_wrapper(callback, output_collector),
prompt,
prompt_template,
_callback_wrapper(callback, output_collector),
**generate_kwargs,
)
self.model.prompt_model(
prompt=prompt,
callback=_callback_wrapper(callback, output_collector),
prompt,
prompt_template,
_callback_wrapper(callback, output_collector),
**generate_kwargs,
)
@@ -423,24 +444,6 @@ class GPT4All:
Formatted prompt.
"""
if isinstance(default_prompt_header, bool):
import warnings
warnings.warn(
"Using True/False for the 'default_prompt_header' is deprecated. Use a string instead.",
DeprecationWarning,
)
default_prompt_header = ""
if isinstance(default_prompt_footer, bool):
import warnings
warnings.warn(
"Using True/False for the 'default_prompt_footer' is deprecated. Use a string instead.",
DeprecationWarning,
)
default_prompt_footer = ""
full_prompt = default_prompt_header + "\n\n" if default_prompt_header != "" else ""
for message in messages:

2
gpt4all-bindings/python/setup.py
View File

@@ -68,7 +68,7 @@ def get_long_description():
setup(
name=package_name,
version="2.2.1.post1",
version="2.3.0",
description="Python bindings for GPT4All",
long_description=get_long_description(),
long_description_content_type="text/markdown",