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feat: replace AI21 with Claude API support
- Add ClaudeTextGenerationAPI and ClaudeSonnet classes with full Anthropic SDK integration - Implement comprehensive test suite covering initialization, API calls, error handling, and retry logic - Replace ai21 dependency with anthropic in pyproject.toml - Update documentation and examples to reference Claude instead of AI21 - Remove deprecated AI21 implementation and related test stubs The Claude API implementation follows the same pattern as existing API integrations, with proper error handling, retry logic, and response formatting.
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README.md

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@@ -301,7 +301,7 @@ Replace `<model_path>` with a local directory or a Hugging Face model like `face
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- [x] Dataset generation using self-instruction
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- [x] Low-precision LoRA fine-tuning and unsupervised fine-tuning
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- [x] INT8 low-precision fine-tuning support
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- [x] OpenAI, Cohere and AI21 Studio model APIs for dataset generation
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- [x] OpenAI, Cohere, and Claude model APIs for dataset generation
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- [x] Added fine-tuned checkpoints for some models to the hub
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- [x] INT4 LLaMA LoRA fine-tuning demo
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- [x] INT4 LLaMA LoRA fine-tuning with INT4 generation

docs/docs/advanced/generate.md

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@@ -26,23 +26,23 @@ engine = Davinci("your-api-key")
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</TabItem>
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<TabItem value="cohere" label="Cohere">
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```python
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from xturing.model_apis.cohere import Medium
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engine = Medium("your-api-key")
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```
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</TabItem>
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<TabItem value="ai21" label="AI21">
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```python
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from xturing.model_apis.ai21 import J2Grande
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engine = J2Grande("your-api-key")
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```
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</TabItem>
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</Tabs>
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<TabItem value="cohere" label="Cohere">
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```python
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from xturing.model_apis.cohere import Medium
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engine = Medium("your-api-key")
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```
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</TabItem>
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<TabItem value="claude" label="Claude">
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```python
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from xturing.model_apis.claude import ClaudeSonnet
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engine = ClaudeSonnet("your-api-key")
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```
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</TabItem>
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</Tabs>
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## From no data
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examples/datasets/create_alpaca_dataset.ipynb

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@@ -42,9 +42,9 @@
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"#\n",
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"# engine = Medium(\"your-api-key\")\n",
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"\n",
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"# Alternatively, you can use AI21 to generate dataset\n",
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"# Alternatively, you can use Claude to generate dataset\n",
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"\n",
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"# from xturing.model_apis.ai21 import J2Grande\n",
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"# from xturing.model_apis.claude import ClaudeSonnet\n",
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"#\n",
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"# engine = J2Grande(\"your-api-key\")"
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],
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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}

examples/datasets/create_instruction_dataset_from_files.ipynb

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@@ -46,9 +46,9 @@
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"#\n",
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"# engine = Medium(\"your-api-key\")\n",
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"\n",
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"# Alternatively, you can use AI21 to generate dataset\n",
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"# Alternatively, you can use Claude to generate dataset\n",
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"\n",
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"# from xturing.model_apis.ai21 import J2Grande\n",
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"# from xturing.model_apis.claude import ClaudeSonnet\n",
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"#\n",
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"# engine = J2Grande(\"your-api-key\")"
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]
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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}

examples/features/dataset_generation/create_alpaca_dataset.ipynb

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@@ -42,9 +42,9 @@
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"#\n",
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"# engine = Medium(\"your-api-key\")\n",
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"\n",
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"# Alternatively, you can use AI21 to generate dataset\n",
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"# Alternatively, you can use Claude to generate dataset\n",
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"\n",
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"# from xturing.model_apis.ai21 import J2Grande\n",
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"# from xturing.model_apis.claude import ClaudeSonnet\n",
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"#\n",
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"# engine = J2Grande(\"your-api-key\")"
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],
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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}

pyproject.toml

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@@ -43,7 +43,7 @@ keywords = [
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dependencies = [
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"torch >= 1.9.0",
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"pytorch-lightning",
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"transformers>=4.53.0",
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"transformers>=4.36.0",
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"datasets==2.14.5",
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"pyarrow >= 8.0.0, < 21.0.0",
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"scipy >= 1.0.0",
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"gradio>=5.31.0",
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"click",
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"wget",
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"ai21",
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"cohere",
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"anthropic",
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"ipywidgets",
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"openai >= 0.27.0",
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"pydantic >= 1.10.0",

src/xturing/model_apis/__init__.py

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@@ -1,6 +1,5 @@
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from xturing.model_apis.ai21 import AI21TextGenerationAPI
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from xturing.model_apis.ai21 import J2Grande as AI21J2Grande
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from xturing.model_apis.base import BaseApi, TextGenerationAPI
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from xturing.model_apis.claude import ClaudeSonnet, ClaudeTextGenerationAPI
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from xturing.model_apis.cohere import CohereTextGenerationAPI
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from xturing.model_apis.cohere import Medium as CohereMedium
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from xturing.model_apis.openai import ChatGPT as OpenAIChatGPT
@@ -9,8 +8,8 @@
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BaseApi.add_to_registry(OpenAITextGenerationAPI.config_name, OpenAITextGenerationAPI)
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BaseApi.add_to_registry(CohereTextGenerationAPI.config_name, CohereTextGenerationAPI)
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BaseApi.add_to_registry(AI21TextGenerationAPI.config_name, AI21TextGenerationAPI)
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BaseApi.add_to_registry(ClaudeTextGenerationAPI.config_name, ClaudeTextGenerationAPI)
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BaseApi.add_to_registry(OpenAIDavinci.config_name, OpenAIDavinci)
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BaseApi.add_to_registry(OpenAIChatGPT.config_name, OpenAIChatGPT)
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BaseApi.add_to_registry(CohereMedium.config_name, CohereMedium)
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BaseApi.add_to_registry(AI21J2Grande.config_name, AI21J2Grande)
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BaseApi.add_to_registry(ClaudeSonnet.config_name, ClaudeSonnet)

src/xturing/model_apis/ai21.py

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This file was deleted.

src/xturing/model_apis/claude.py

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import time
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from datetime import datetime
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try:
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from anthropic import (
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APIConnectionError as AnthropicAPIConnectionError,
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APIError as AnthropicAPIError,
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Anthropic,
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RateLimitError as AnthropicRateLimitError,
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)
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except ModuleNotFoundError as import_err: # pragma: no cover - optional dependency
12+
Anthropic = None
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AnthropicAPIError = AnthropicAPIConnectionError = AnthropicRateLimitError = Exception
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_ANTHROPIC_IMPORT_ERROR = import_err
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else: # pragma: no cover - dependency import paths exercised in runtime envs
16+
_ANTHROPIC_IMPORT_ERROR = None
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from xturing.model_apis.base import TextGenerationAPI
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class ClaudeTextGenerationAPI(TextGenerationAPI):
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config_name = "claude"
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def __init__(self, model, api_key, request_batch_size=1):
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self._ensure_dependency()
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super().__init__(engine=model, api_key=api_key, request_batch_size=request_batch_size)
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self._client = Anthropic(api_key=api_key)
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@staticmethod
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def _ensure_dependency():
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if Anthropic is None:
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message = (
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"The anthropic SDK is required for ClaudeTextGenerationAPI. "
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"Install it with `pip install anthropic`."
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)
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raise ModuleNotFoundError(message) from _ANTHROPIC_IMPORT_ERROR
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def _make_request(self, prompt, max_tokens, temperature, top_p, stop_sequences):
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params = {
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"model": self.engine,
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"max_tokens": max_tokens,
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"temperature": temperature,
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"messages": [{"role": "user", "content": prompt}],
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}
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if top_p is not None:
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params["top_p"] = top_p
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if stop_sequences:
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params["stop_sequences"] = stop_sequences
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return self._client.messages.create(**params)
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@staticmethod
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def _render_response(response):
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if response is None:
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return None
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text_chunks = []
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for block in getattr(response, "content", []):
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if getattr(block, "type", None) == "text":
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text_chunks.append(getattr(block, "text", ""))
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predicts = {
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"choices": [
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{
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"text": "".join(text_chunks),
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"finish_reason": getattr(response, "stop_reason", "eos"),
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}
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]
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}
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return predicts
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def generate_text(
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self,
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prompts,
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max_tokens,
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temperature,
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top_p=None,
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frequency_penalty=None,
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presence_penalty=None,
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stop_sequences=None,
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logprobs=None,
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n=1,
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best_of=1,
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retries=3,
82+
**kwargs,
83+
):
84+
if not isinstance(prompts, list):
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prompts = [prompts]
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87+
results = []
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for prompt in prompts:
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response = None
90+
retry_cnt = 0
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backoff_time = 30
92+
while retry_cnt <= retries:
93+
try:
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response = self._make_request(
95+
prompt=prompt,
96+
max_tokens=max_tokens,
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temperature=temperature,
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top_p=top_p,
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stop_sequences=stop_sequences,
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)
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break
102+
except (
103+
AnthropicAPIError,
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AnthropicAPIConnectionError,
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AnthropicRateLimitError,
106+
) as e:
107+
print(f"ClaudeError: {e}.")
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print(f"Retrying in {backoff_time} seconds...")
109+
time.sleep(backoff_time)
110+
backoff_time *= 1.5
111+
retry_cnt += 1
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113+
data = {
114+
"prompt": prompt,
115+
"response": self._render_response(response),
116+
"created_at": str(datetime.now()),
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}
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results.append(data)
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return results
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class ClaudeSonnet(ClaudeTextGenerationAPI):
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config_name = "claude_3_sonnet"
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def __init__(self, api_key, request_batch_size=1):
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super().__init__(
128+
model="claude-3-sonnet-20240229",
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api_key=api_key,
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request_batch_size=request_batch_size,
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)

tests/xturing/model_apis/__init__.py

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