Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Gpt4free | 45,225 | 12 hours ago | 47 | gpl-3.0 | Python | |||||
The official gpt4free repository | various collection of powerful language models | ||||||||||
Chatrwkv | 8,755 | 1 | 9 days ago | 33 | August 05, 2023 | 38 | apache-2.0 | Python | ||
ChatRWKV is like ChatGPT but powered by RWKV (100% RNN) language model, and open source. | ||||||||||
Petals | 7,538 | 5 days ago | 14 | August 03, 2023 | 69 | mit | Python | |||
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading | ||||||||||
Internlm | 3,156 | 14 hours ago | 43 | apache-2.0 | Python | |||||
InternLM has open-sourced a 7 and 20 billion parameter base models and chat models tailored for practical scenarios and the training system. | ||||||||||
Linly | 2,698 | a month ago | 102 | Python | ||||||
Chinese-LLaMA 1&2、Chinese-Falcon 基础模型;ChatFlow中文对话模型;中文OpenLLaMA模型;NLP预训练/指令微调数据集 | ||||||||||
Easylm | 1,957 | 17 hours ago | 17 | apache-2.0 | Python | |||||
Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax. | ||||||||||
Skychat Chinese Chatbot Gpt3 | 543 | 6 months ago | 4 | mit | C# | |||||
SkyChat是一款基于中文GPT-3 api的聊天机器人项目。它可以像chatGPT一样,实现人机聊天、问答、中英文互译、对对联、写古诗等任务。| SkyChat is a Chatbot project based on Chinese GPT3 API. Like chatGPT, it can do human-machine chat, question and answer, and can also complete tasks such as Chinese-English or English-Chinese translation, content continuation, couplets, and Chinese ancient poems writing. | ||||||||||
Chatgpt Prompts | 520 | 4 months ago | cc0-1.0 | |||||||
ChatGPT and Bing AI prompt curation | ||||||||||
Tf_chatbot_seq2seq_antilm | 373 | 7 years ago | 20 | Python | ||||||
Seq2seq chatbot with attention and anti-language model to suppress generic response, option for further improve by deep reinforcement learning. | ||||||||||
Pykoi | 304 | 2 days ago | 1 | apache-2.0 | Python | |||||
pykoi: Active learning in one unified interface |
By using this repository or any code related to it, you agree to the legal notice. The author is not responsible for any copies, forks, reuploads made by other users, or anything else related to gpt4free. This is the author's only account and repository. To prevent impersonation or irresponsible actions, please comply with the GNU GPL license this Repository uses.
pip install -U g4f
pip install -U g4f
git clone https://github.com/xtekky/gpt4free.git
cd gpt4free
python3 -m venv venv
.\venv\Scripts\activate
source venv/bin/activate
requirements.txt
:pip install -r requirements.txt
test.py
file in the root folder and start using the repo, further Instructions are belowimport g4f
...
If you have Docker installed, you can easily set up and run the project without manually installing dependencies.
First, ensure you have both Docker and Docker Compose installed.
Clone the GitHub repo:
git clone https://github.com/xtekky/gpt4free.git
cd gpt4free
docker compose build
docker compose up
You server will now be running at http://localhost:1337
. You can interact with the API or run your tests as you would normally.
To stop the Docker containers, simply run:
docker compose down
Note: When using Docker, any changes you make to your local files will be reflected in the Docker container thanks to the volume mapping in the docker-compose.yml
file. If you add or remove dependencies, however, you'll need to rebuild the Docker image using docker compose build
.
g4f
Packageimport g4f
print(g4f.Provider.Ails.params) # supported args
# Automatic selection of provider
# streamed completion
response = g4f.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hello world"}],
stream=True,
)
for message in response:
print(message, flush=True, end='')
# normal response
response = g4f.ChatCompletion.create(
model=g4f.models.gpt_4,
messages=[{"role": "user", "content": "hi"}],
) # alterative model setting
print(response)
# Set with provider
response = g4f.ChatCompletion.create(
model="gpt-3.5-turbo",
provider=g4f.Provider.DeepAi,
messages=[{"role": "user", "content": "Hello world"}],
stream=True,
)
for message in response:
print(message)
import g4f
allowed_models = [
'code-davinci-002',
'text-ada-001',
'text-babbage-001',
'text-curie-001',
'text-davinci-002',
'text-davinci-003'
]
response = g4f.Completion.create(
model = 'text-davinci-003',
prompt = 'say this is a test')
print(response)
from g4f.Provider import (
AItianhu,
Acytoo,
Aichat,
Ails,
Aivvm,
Bard,
Bing,
ChatBase,
ChatgptAi,
ChatgptLogin,
CodeLinkAva,
DeepAi,
H2o,
HuggingChat,
Opchatgpts,
OpenAssistant,
OpenaiChat,
Raycast,
Theb,
Vercel,
Vitalentum,
Wewordle,
Ylokh,
You,
Yqcloud,
)
# Usage:
response = g4f.ChatCompletion.create(..., provider=ProviderName)
Cookies are essential for the proper functioning of some service providers. It is imperative to maintain an active session, typically achieved by logging into your account.
When running the g4f package locally, the package automatically retrieves cookies from your web browser using the get_cookies
function. However, if you're not running it locally, you'll need to provide the cookies manually by passing them as parameters using the cookies
parameter.
import g4f
from g4f.Provider import (
Bard,
Bing,
HuggingChat,
OpenAssistant,
OpenaiChat,
)
# Usage:
response = g4f.ChatCompletion.create(
model=g4f.models.default,
messages=[{"role": "user", "content": "Hello"}],
provider=Bard,
#cookies=g4f.get_cookies(".google.com"),
cookies={"cookie_name": "value", "cookie_name2": "value2"},
auth=True
)
To enhance speed and overall performance, execute providers asynchronously. The total execution time will be determined by the duration of the slowest provider's execution.
import g4f, asyncio
_providers = [
g4f.Provider.Aichat,
g4f.Provider.Aivvm,
g4f.Provider.ChatBase,
g4f.Provider.Bing,
g4f.Provider.CodeLinkAva,
g4f.Provider.DeepAi,
g4f.Provider.GptGo,
g4f.Provider.Wewordle,
g4f.Provider.You,
g4f.Provider.Yqcloud,
]
async def run_provider(provider: g4f.Provider.AsyncProvider):
try:
response = await provider.create_async(
model=g4f.models.default.name,
messages=[{"role": "user", "content": "Hello"}],
)
print(f"{provider.__name__}:", response)
except Exception as e:
print(f"{provider.__name__}:", e)
async def run_all():
calls = [
run_provider(provider) for provider in _providers
]
await asyncio.gather(*calls)
asyncio.run(run_all())
If you want to use the embedding function, you need to get a huggingface token. You can get one at https://huggingface.co/settings/tokens make sure your role is set to write. If you have your token, just use it instead of the OpenAI api-key.
get requirements:
pip install -r interference/requirements.txt
run server:
python3 -m interference.app
import openai
openai.api_key = "Empty if you don't use embeddings, otherwise your hugginface token"
openai.api_base = "http://localhost:1337"
def main():
chat_completion = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "write a poem about a tree"}],
stream=True,
)
if isinstance(chat_completion, dict):
# not stream
print(chat_completion.choices[0].message.content)
else:
# stream
for token in chat_completion:
content = token["choices"][0]["delta"].get("content")
if content != None:
print(content, end="", flush=True)
if __name__ == "__main__":
main()
Website | Provider | gpt-3.5 | gpt-4 | Streaming | Asynchron | Status | Auth |
---|---|---|---|---|---|---|---|
www.aitianhu.com | g4f.Provider.AItianhu |
||||||
chat.acytoo.com | g4f.Provider.Acytoo |
||||||
chat-gpt.org | g4f.Provider.Aichat |
||||||
ai.ls | g4f.Provider.Ails |
||||||
chat.aivvm.com | g4f.Provider.Aivvm |
||||||
bard.google.com | g4f.Provider.Bard |
||||||
bing.com | g4f.Provider.Bing |
||||||
www.chatbase.co | g4f.Provider.ChatBase |
||||||
chatgpt.ai | g4f.Provider.ChatgptAi |
||||||
opchatgpts.net | g4f.Provider.ChatgptLogin |
||||||
ava-ai-ef611.web.app | g4f.Provider.CodeLinkAva |
||||||
deepai.org | g4f.Provider.DeepAi |
||||||
gptgo.ai | g4f.Provider.GptGo |
||||||
gpt-gm.h2o.ai | g4f.Provider.H2o |
||||||
huggingface.co | g4f.Provider.HuggingChat |
||||||
opchatgpts.net | g4f.Provider.Opchatgpts |
||||||
open-assistant.io | g4f.Provider.OpenAssistant |
||||||
chat.openai.com | g4f.Provider.OpenaiChat |
||||||
www.perplexity.ai | g4f.Provider.PerplexityAi |
||||||
raycast.com | g4f.Provider.Raycast |
||||||
theb.ai | g4f.Provider.Theb |
||||||
sdk.vercel.ai | g4f.Provider.Vercel |
||||||
app.vitalentum.io | g4f.Provider.Vitalentum |
||||||
wewordle.org | g4f.Provider.Wewordle |
||||||
chat.ylokh.xyz | g4f.Provider.Ylokh |
||||||
you.com | g4f.Provider.You |
||||||
chat9.yqcloud.top | g4f.Provider.Yqcloud |
||||||
aiservice.vercel.app | g4f.Provider.AiService |
||||||
chat.dfehub.com | g4f.Provider.DfeHub |
||||||
free.easychat.work | g4f.Provider.EasyChat |
||||||
next.eqing.tech | g4f.Provider.Equing |
||||||
chat9.fastgpt.me | g4f.Provider.FastGpt |
||||||
forefront.com | g4f.Provider.Forefront |
||||||
chat.getgpt.world | g4f.Provider.GetGpt |
||||||
liaobots.com | g4f.Provider.Liaobots |
||||||
p5.v50.ltd | g4f.Provider.V50 |
||||||
chat.wuguokai.xyz | g4f.Provider.Wuguokai |
Model | Base Provider | Provider | Website |
---|---|---|---|
palm | g4f.Provider.Bard | bard.google.com | |
h2ogpt-gm-oasst1-en-2048-falcon-7b-v3 | Huggingface | g4f.Provider.H2o | www.h2o.ai |
h2ogpt-gm-oasst1-en-2048-falcon-40b-v1 | Huggingface | g4f.Provider.H2o | www.h2o.ai |
h2ogpt-gm-oasst1-en-2048-open-llama-13b | Huggingface | g4f.Provider.H2o | www.h2o.ai |
claude-instant-v1 | Anthropic | g4f.Provider.Vercel | sdk.vercel.ai |
claude-v1 | Anthropic | g4f.Provider.Vercel | sdk.vercel.ai |
claude-v2 | Anthropic | g4f.Provider.Vercel | sdk.vercel.ai |
command-light-nightly | Cohere | g4f.Provider.Vercel | sdk.vercel.ai |
command-nightly | Cohere | g4f.Provider.Vercel | sdk.vercel.ai |
gpt-neox-20b | Huggingface | g4f.Provider.Vercel | sdk.vercel.ai |
oasst-sft-1-pythia-12b | Huggingface | g4f.Provider.Vercel | sdk.vercel.ai |
oasst-sft-4-pythia-12b-epoch-3.5 | Huggingface | g4f.Provider.Vercel | sdk.vercel.ai |
santacoder | Huggingface | g4f.Provider.Vercel | sdk.vercel.ai |
bloom | Huggingface | g4f.Provider.Vercel | sdk.vercel.ai |
flan-t5-xxl | Huggingface | g4f.Provider.Vercel | sdk.vercel.ai |
code-davinci-002 | OpenAI | g4f.Provider.Vercel | sdk.vercel.ai |
gpt-3.5-turbo-16k | OpenAI | g4f.Provider.Vercel | sdk.vercel.ai |
gpt-3.5-turbo-16k-0613 | OpenAI | g4f.Provider.Vercel | sdk.vercel.ai |
gpt-4-0613 | OpenAI | g4f.Provider.Vercel | sdk.vercel.ai |
text-ada-001 | OpenAI | g4f.Provider.Vercel | sdk.vercel.ai |
text-babbage-001 | OpenAI | g4f.Provider.Vercel | sdk.vercel.ai |
text-curie-001 | OpenAI | g4f.Provider.Vercel | sdk.vercel.ai |
text-davinci-002 | OpenAI | g4f.Provider.Vercel | sdk.vercel.ai |
text-davinci-003 | OpenAI | g4f.Provider.Vercel | sdk.vercel.ai |
llama13b-v2-chat | Replicate | g4f.Provider.Vercel | sdk.vercel.ai |
llama7b-v2-chat | Replicate | g4f.Provider.Vercel | sdk.vercel.ai |
Projects | Stars | Forks | Issues | Pull requests |
gpt4free | ||||
gpt4free-ts | ||||
ChatGPT-Clone | ||||
ChatGpt Discord Bot | ||||
LangChain gpt4free | ||||
ChatGpt Telegram Bot | ||||
Action Translate Readme | ||||
Langchain Document GPT |
to add another provider, its very simple:
from .base_provider import BaseProvider
from ..typing import CreateResult, Any
class HogeService(BaseProvider):
url = "http://hoge.com"
working = True
supports_gpt_35_turbo = True
@staticmethod
def create_completion(
model: str,
messages: list[dict[str, str]],
stream: bool,
**kwargs: Any,
) -> CreateResult:
pass
working
to True
...create_completion
and yield
the response, even if its a one-time response, do not hesitate to look at other providers for inspirationfrom .base_provider import BaseProvider
from .HogeService import HogeService
__all__ = [
HogeService,
]
import g4f
response = g4f.ChatCompletion.create(model='gpt-3.5-turbo', provider=g4f.Provider.PROVIDERNAME,
messages=[{"role": "user", "content": "test"}], stream=g4f.Provider.PROVIDERNAME.supports_stream)
for message in response:
print(message, flush=True, end='')
A list of the contributors is available here
The Vercel.py
file contains code from vercel-llm-api by @ading2210, which is licenced under the GNU GPL v3
Top 1 Contributor: @hlohaus
This program is licensed under the GNU GPL v3
xtekky/gpt4free: Copyright (C) 2023 xtekky
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.