forked from Archives/langchain
Harrison/gpt4all (#2366)
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This commit is contained in:
parent
d17dea30ce
commit
0a9f04bad9
37
docs/ecosystem/gpt4all.md
Normal file
37
docs/ecosystem/gpt4all.md
Normal file
@ -0,0 +1,37 @@
|
||||
# GPT4All
|
||||
|
||||
This page covers how to use the `GPT4All` wrapper within LangChain.
|
||||
It is broken into two parts: installation and setup, and then usage with an example.
|
||||
|
||||
## Installation and Setup
|
||||
- Install the Python package with `pip install pyllamacpp`
|
||||
- Download a [GPT4All model](https://github.com/nomic-ai/gpt4all) and place it in your desired directory
|
||||
|
||||
## Usage
|
||||
|
||||
### GPT4All
|
||||
|
||||
To use the GPT4All wrapper, you need to provide the path to the pre-trained model file and the model's configuration.
|
||||
```python
|
||||
from langchain.llms import GPT4All
|
||||
|
||||
# Instantiate the model
|
||||
model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)
|
||||
|
||||
# Generate text
|
||||
response = model("Once upon a time, ")
|
||||
```
|
||||
|
||||
You can also customize the generation parameters, such as n_predict, temp, top_p, top_k, and others.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
model = GPT4All(model="./models/gpt4all-model.bin", n_predict=55, temp=0)
|
||||
response = model("Once upon a time, ")
|
||||
```
|
||||
## Model File
|
||||
|
||||
You can find links to model file downloads at the [GPT4all](https://github.com/nomic-ai/gpt4all) repository. They will need to be converted to `ggml` format to work, as specified in the [pyllamacpp](https://github.com/nomic-ai/pyllamacpp) repository.
|
||||
|
||||
For a more detailed walkthrough of this, see [this notebook](../modules/models/llms/integrations/gpt4all.ipynb)
|
85
docs/modules/models/llms/integrations/gpt4all.ipynb
Normal file
85
docs/modules/models/llms/integrations/gpt4all.ipynb
Normal file
@ -0,0 +1,85 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# OpenAI\n",
|
||||
"\n",
|
||||
"This example goes over how to use LangChain to interact with GPT4All models"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install pyllamacpp"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.llms import GPT4All\n",
|
||||
"from langchain import PromptTemplate, LLMChain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"template = \"\"\"Question: {question}\n",
|
||||
"\n",
|
||||
"Answer: Let's think step by step.\"\"\"\n",
|
||||
"\n",
|
||||
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# You'll need to download a compatible model and convert it to ggml.\n",
|
||||
"# See: https://github.com/nomic-ai/gpt4all for more information.\n",
|
||||
"llm = GPT4All(model_path=\"./models/gpt4all-model.bin\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"llm_chain = LLMChain(prompt=prompt, llm=llm)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
|
||||
"\n",
|
||||
"llm_chain.run(question)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
@ -11,6 +11,7 @@ from langchain.llms.cohere import Cohere
|
||||
from langchain.llms.deepinfra import DeepInfra
|
||||
from langchain.llms.forefrontai import ForefrontAI
|
||||
from langchain.llms.gooseai import GooseAI
|
||||
from langchain.llms.gpt4all import GPT4All
|
||||
from langchain.llms.huggingface_endpoint import HuggingFaceEndpoint
|
||||
from langchain.llms.huggingface_hub import HuggingFaceHub
|
||||
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
|
||||
@ -36,6 +37,7 @@ __all__ = [
|
||||
"DeepInfra",
|
||||
"ForefrontAI",
|
||||
"GooseAI",
|
||||
"GPT4All",
|
||||
"LlamaCpp",
|
||||
"Modal",
|
||||
"NLPCloud",
|
||||
@ -67,6 +69,7 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
|
||||
"deepinfra": DeepInfra,
|
||||
"forefrontai": ForefrontAI,
|
||||
"gooseai": GooseAI,
|
||||
"gpt4all": GPT4All,
|
||||
"huggingface_hub": HuggingFaceHub,
|
||||
"huggingface_endpoint": HuggingFaceEndpoint,
|
||||
"llamacpp": LlamaCpp,
|
||||
|
183
langchain/llms/gpt4all.py
Normal file
183
langchain/llms/gpt4all.py
Normal file
@ -0,0 +1,183 @@
|
||||
"""Wrapper for the GPT4All model."""
|
||||
from typing import Any, Dict, List, Mapping, Optional, Set
|
||||
|
||||
from pydantic import BaseModel, Extra, Field, root_validator
|
||||
|
||||
from langchain.llms.base import LLM
|
||||
from langchain.llms.utils import enforce_stop_tokens
|
||||
|
||||
|
||||
class GPT4All(LLM, BaseModel):
|
||||
r"""Wrapper around GPT4All language models.
|
||||
|
||||
To use, you should have the ``pyllamacpp`` python package installed, the
|
||||
pre-trained model file, and the model's config information.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.llms import GPT4All
|
||||
model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)
|
||||
|
||||
# Simplest invocation
|
||||
response = model("Once upon a time, ")
|
||||
"""
|
||||
|
||||
model: str
|
||||
"""Path to the pre-trained GPT4All model file."""
|
||||
|
||||
n_ctx: int = Field(512, alias="n_ctx")
|
||||
"""Token context window."""
|
||||
|
||||
n_parts: int = Field(-1, alias="n_parts")
|
||||
"""Number of parts to split the model into.
|
||||
If -1, the number of parts is automatically determined."""
|
||||
|
||||
seed: int = Field(0, alias="seed")
|
||||
"""Seed. If -1, a random seed is used."""
|
||||
|
||||
f16_kv: bool = Field(False, alias="f16_kv")
|
||||
"""Use half-precision for key/value cache."""
|
||||
|
||||
logits_all: bool = Field(False, alias="logits_all")
|
||||
"""Return logits for all tokens, not just the last token."""
|
||||
|
||||
vocab_only: bool = Field(False, alias="vocab_only")
|
||||
"""Only load the vocabulary, no weights."""
|
||||
|
||||
use_mlock: bool = Field(False, alias="use_mlock")
|
||||
"""Force system to keep model in RAM."""
|
||||
|
||||
embedding: bool = Field(False, alias="embedding")
|
||||
"""Use embedding mode only."""
|
||||
|
||||
n_threads: Optional[int] = Field(4, alias="n_threads")
|
||||
"""Number of threads to use."""
|
||||
|
||||
n_predict: Optional[int] = 256
|
||||
"""The maximum number of tokens to generate."""
|
||||
|
||||
temp: Optional[float] = 0.8
|
||||
"""The temperature to use for sampling."""
|
||||
|
||||
top_p: Optional[float] = 0.95
|
||||
"""The top-p value to use for sampling."""
|
||||
|
||||
top_k: Optional[int] = 40
|
||||
"""The top-k value to use for sampling."""
|
||||
|
||||
echo: Optional[bool] = False
|
||||
"""Whether to echo the prompt."""
|
||||
|
||||
stop: Optional[List[str]] = []
|
||||
"""A list of strings to stop generation when encountered."""
|
||||
|
||||
repeat_last_n: Optional[int] = 64
|
||||
"Last n tokens to penalize"
|
||||
|
||||
repeat_penalty: Optional[float] = 1.3
|
||||
"""The penalty to apply to repeated tokens."""
|
||||
|
||||
n_batch: int = Field(1, alias="n_batch")
|
||||
"""Batch size for prompt processing."""
|
||||
|
||||
streaming: bool = False
|
||||
"""Whether to stream the results or not."""
|
||||
|
||||
client: Any = None #: :meta private:
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
@property
|
||||
def _default_params(self) -> Dict[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {
|
||||
"seed": self.seed,
|
||||
"n_predict": self.n_predict,
|
||||
"n_threads": self.n_threads,
|
||||
"n_batch": self.n_batch,
|
||||
"repeat_last_n": self.repeat_last_n,
|
||||
"repeat_penalty": self.repeat_penalty,
|
||||
"top_k": self.top_k,
|
||||
"top_p": self.top_p,
|
||||
"temp": self.temp,
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _llama_param_names() -> Set[str]:
|
||||
"""Get the identifying parameters."""
|
||||
return {
|
||||
"seed",
|
||||
"n_ctx",
|
||||
"n_parts",
|
||||
"f16_kv",
|
||||
"logits_all",
|
||||
"vocab_only",
|
||||
"use_mlock",
|
||||
"embedding",
|
||||
}
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that the python package exists in the environment."""
|
||||
try:
|
||||
from pyllamacpp.model import Model as GPT4AllModel
|
||||
|
||||
llama_keys = cls._llama_param_names()
|
||||
model_kwargs = {k: v for k, v in values.items() if k in llama_keys}
|
||||
values["client"] = GPT4AllModel(
|
||||
ggml_model=values["model"],
|
||||
**model_kwargs,
|
||||
)
|
||||
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import pyllamacpp python package. "
|
||||
"Please install it with `pip install pyllamacpp`."
|
||||
)
|
||||
return values
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {
|
||||
"model": self.model,
|
||||
**self._default_params,
|
||||
**{
|
||||
k: v
|
||||
for k, v in self.__dict__.items()
|
||||
if k in GPT4All._llama_param_names()
|
||||
},
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return the type of llm."""
|
||||
return "gpt4all"
|
||||
|
||||
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
|
||||
r"""Call out to GPT4All's generate method.
|
||||
|
||||
Args:
|
||||
prompt: The prompt to pass into the model.
|
||||
stop: A list of strings to stop generation when encountered.
|
||||
|
||||
Returns:
|
||||
The string generated by the model.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
prompt = "Once upon a time, "
|
||||
response = model(prompt, n_predict=55)
|
||||
"""
|
||||
text = self.client.generate(
|
||||
prompt,
|
||||
**self._default_params,
|
||||
)
|
||||
if stop is not None:
|
||||
text = enforce_stop_tokens(text, stop)
|
||||
return text
|
34
tests/integration_tests/llms/test_gpt4all.py
Normal file
34
tests/integration_tests/llms/test_gpt4all.py
Normal file
@ -0,0 +1,34 @@
|
||||
# flake8: noqa
|
||||
"""Test Llama.cpp wrapper."""
|
||||
import os
|
||||
from urllib.request import urlretrieve
|
||||
|
||||
from langchain.llms import GPT4All
|
||||
|
||||
|
||||
def _download_model() -> str:
|
||||
"""Download model.
|
||||
From https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized.bin,
|
||||
convert to new ggml format and return model path."""
|
||||
model_url = "https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized.bin"
|
||||
tokenizer_url = "https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/tokenizer.model"
|
||||
conversion_script = "https://github.com/nomic-ai/pyllamacpp/blob/main/pyllamacpp/scripts/convert_gpt4all.py"
|
||||
local_filename = model_url.split("/")[-1]
|
||||
|
||||
if not os.path.exists("convert_gpt4all.py"):
|
||||
urlretrieve(conversion_script, "convert_gpt4all.py")
|
||||
if not os.path.exists("tokenizer.model"):
|
||||
urlretrieve(tokenizer_url, "tokenizer.model")
|
||||
if not os.path.exists(local_filename):
|
||||
urlretrieve(model_url, local_filename)
|
||||
os.system(f"python convert_gpt4all.py.py . tokenizer.model")
|
||||
|
||||
return local_filename
|
||||
|
||||
|
||||
def test_gpt4all_inference() -> None:
|
||||
"""Test valid gpt4all inference."""
|
||||
model_path = _download_model()
|
||||
llm = GPT4All(model=model_path)
|
||||
output = llm("Say foo:")
|
||||
assert isinstance(output, str)
|
Loading…
Reference in New Issue
Block a user