diff options
| -rw-r--r-- | README.md | 3 | ||||
| -rw-r--r-- | g4f/Provider/Providers/Vercel.py | 117 | ||||
| -rw-r--r-- | setup.py | 2 |
3 files changed, 11 insertions, 111 deletions
@@ -1,4 +1,5 @@ - + + By using this repository or any code related to it, you agree to the [legal notice](./LEGAL_NOTICE.md). The author is not responsible for any copies, forks, or reuploads made by other users. This is the author's only account and repository. To prevent impersonation or irresponsible actions, you may comply with the GNU GPL license this Repository uses. diff --git a/g4f/Provider/Providers/Vercel.py b/g4f/Provider/Providers/Vercel.py index e1e84f9a..03d9be17 100644 --- a/g4f/Provider/Providers/Vercel.py +++ b/g4f/Provider/Providers/Vercel.py @@ -42,120 +42,19 @@ vercel_models = {'anthropic:claude-instant-v1': {'id': 'anthropic:claude-instant 'id': 'huggingface:bigcode/santacoder', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigCode', 'instructions': 'The model was trained on GitHub code. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. # the following function computes the sqrt) or write a function signature and docstring and let the model complete the function body.', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'santacoder'}, 'cohere:command-medium-nightly': {'id': 'cohere:command-medium-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-medium-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'cohere:command-xlarge-nightly': {'id': 'cohere:command-xlarge-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-xlarge-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:gpt-4': {'id': 'openai:gpt-4', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'gpt-4', 'minBillingTier': 'pro', 'parameters': {'temperature': {'value': 0.7, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:code-cushman-001': {'id': 'openai:code-cushman-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-cushman-001'}, 'openai:code-davinci-002': {'id': 'openai:code-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-davinci-002'}, 'openai:gpt-3.5-turbo': {'id': 'openai:gpt-3.5-turbo', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.7, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'gpt-3.5-turbo'}, 'openai:text-ada-001': {'id': 'openai:text-ada-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-ada-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-babbage-001': {'id': 'openai:text-babbage-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-babbage-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-curie-001': {'id': 'openai:text-curie-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-curie-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-002': {'id': 'openai:text-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-002', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-003': {'id': 'openai:text-davinci-003', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-003', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}} -# based on https://github.com/ading2210/vercel-llm-api // modified -class Client: - def __init__(self): - self.session = requests.Session() - self.headers = { - 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110 Safari/537.36', - 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8', - 'Accept-Encoding': 'gzip, deflate, br', - 'Accept-Language': 'en-US,en;q=0.5', - 'Te': 'trailers', - 'Upgrade-Insecure-Requests': '1' - } - self.session.headers.update(self.headers) - - def get_token(self): - b64 = self.session.get('https://sdk.vercel.ai/openai.jpeg').text - data = json.loads(base64.b64decode(b64)) - - code = 'const globalThis = {data: `sentinel`}; function token() {return (%s)(%s)}' % ( - data['c'], data['a']) - - token_string = json.dumps(separators=(',', ':'), - obj={'r': execjs.compile(code).call('token'), 't': data['t']}) - - return base64.b64encode(token_string.encode()).decode() - - def get_default_params(self, model_id): - return {key: param['value'] for key, param in vercel_models[model_id]['parameters'].items()} - - def generate(self, model_id: str, prompt: str, params: dict = {}): - if not ':' in model_id: - model_id = models[model_id] - - defaults = self.get_default_params(model_id) - - payload = defaults | params | { - 'prompt': prompt, - 'model': model_id, - } - - headers = self.headers | { - 'Accept-Encoding': 'gzip, deflate, br', - 'Custom-Encoding': self.get_token(), - 'Host': 'sdk.vercel.ai', - 'Origin': 'https://sdk.vercel.ai', - 'Referrer': 'https://sdk.vercel.ai', - 'Sec-Fetch-Dest': 'empty', - 'Sec-Fetch-Mode': 'cors', - 'Sec-Fetch-Site': 'same-origin', - } - - chunks_queue = queue.Queue() - error = None - response = None - - def callback(data): - chunks_queue.put(data.decode()) - - def request_thread(): - nonlocal response, error - for _ in range(3): - try: - response = self.session.post('https://sdk.vercel.ai/api/generate', - json=payload, headers=headers, content_callback=callback) - response.raise_for_status() - - except Exception as e: - if _ == 2: - error = e - - else: - continue - - thread = threading.Thread(target=request_thread, daemon=True) - thread.start() - - text = '' - index = 0 - while True: - try: - chunk = chunks_queue.get(block=True, timeout=0.1) - - except queue.Empty: - if error: - raise error - - elif response: - break - - else: - continue - - text += chunk - lines = text.split('\n') - - if len(lines) - 1 > index: - new = lines[index:-1] - for word in new: - yield json.loads(word) - index = len(lines) - 1 - def _create_completion(model: str, messages: list, stream: bool, **kwargs): + return + # conversation = 'This is a conversation between a human and a language model, respond to the last message accordingly, referring to the past history of messages if needed.\n' - conversation = 'This is a conversation between a human and a language model, respond to the last message accordingly, referring to the past history of messages if needed.\n' - - for message in messages: - conversation += '%s: %s\n' % (message['role'], message['content']) + # for message in messages: + # conversation += '%s: %s\n' % (message['role'], message['content']) - conversation += 'assistant: ' + # conversation += 'assistant: ' - completion = Client().generate(model, conversation) + # completion = Client().generate(model, conversation) - for token in completion: - yield token + # for token in completion: + # yield token params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \ '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file @@ -10,7 +10,7 @@ with codecs.open(os.path.join(here, "README.md"), encoding="utf-8") as fh: with open('requirements.txt') as f: required = f.read().splitlines() -VERSION = '0.0.1.2' +VERSION = '0.0.1.3' DESCRIPTION = 'The official gpt4free repository | various collection of powerful language models' # Setting up |