| 36 | |
| 37 | |
| 38 | class AnthropicLM(BaseLM): |
| 39 | REQ_CHUNK_SIZE = 20 |
| 40 | |
| 41 | def __init__(self, model): |
| 42 | """ |
| 43 | |
| 44 | :param model: str |
| 45 | Anthropic model e.g. claude-instant-v1 |
| 46 | """ |
| 47 | super().__init__() |
| 48 | import anthropic |
| 49 | self.model = model |
| 50 | self.client = anthropic.Client(os.environ['ANTHROPIC_API_KEY']) |
| 51 | |
| 52 | @property |
| 53 | def eot_token_id(self): |
| 54 | raise NotImplementedError("No idea about anthropic tokenization.") |
| 55 | |
| 56 | @property |
| 57 | def max_length(self): |
| 58 | return 2048 |
| 59 | |
| 60 | @property |
| 61 | def max_gen_toks(self): |
| 62 | return 256 |
| 63 | |
| 64 | @property |
| 65 | def batch_size(self): |
| 66 | # Isn't used because we override _loglikelihood_tokens |
| 67 | raise NotImplementedError() |
| 68 | |
| 69 | @property |
| 70 | def device(self): |
| 71 | # Isn't used because we override _loglikelihood_tokens |
| 72 | raise NotImplementedError() |
| 73 | |
| 74 | def tok_encode(self, string: str): |
| 75 | raise NotImplementedError("No idea about anthropic tokenization.") |
| 76 | |
| 77 | def tok_decode(self, tokens): |
| 78 | raise NotImplementedError("No idea about anthropic tokenization.") |
| 79 | |
| 80 | def _loglikelihood_tokens(self, requests, disable_tqdm=False): |
| 81 | raise NotImplementedError("No support for logits.") |
| 82 | |
| 83 | def greedy_until(self, requests): |
| 84 | if not requests: |
| 85 | return [] |
| 86 | |
| 87 | res = [] |
| 88 | for request in tqdm(requests): |
| 89 | inp = request[0] |
| 90 | request_args = request[1] |
| 91 | until = request_args["until"] |
| 92 | response = anthropic_completion( |
| 93 | client=self.client, |
| 94 | model=self.model, |
| 95 | prompt=inp, |
nothing calls this directly
no outgoing calls
no test coverage detected