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chore: optimize ark model parameters (#7378)
This commit is contained in:
sino
GitHub
parent
6cd8ab0cbc
commit
a0a67873aa
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@ -35,7 +35,10 @@ from core.model_runtime.model_providers.volcengine_maas.errors import (
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RateLimitErrors,
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ServerUnavailableErrors,
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)
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from core.model_runtime.model_providers.volcengine_maas.llm.models import ModelConfigs
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from core.model_runtime.model_providers.volcengine_maas.llm.models import (
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get_model_config,
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get_v2_req_params,
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)
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from core.model_runtime.model_providers.volcengine_maas.volc_sdk import MaasException
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logger = logging.getLogger(__name__)
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@ -95,37 +98,12 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
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-> LLMResult | Generator:
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client = MaaSClient.from_credential(credentials)
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req_params = ModelConfigs.get(
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credentials['base_model_name'], {}).get('req_params', {}).copy()
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if credentials.get('context_size'):
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req_params['max_prompt_tokens'] = credentials.get('context_size')
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if credentials.get('max_tokens'):
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req_params['max_new_tokens'] = credentials.get('max_tokens')
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if model_parameters.get('max_tokens'):
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req_params['max_new_tokens'] = model_parameters.get('max_tokens')
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if model_parameters.get('temperature'):
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req_params['temperature'] = model_parameters.get('temperature')
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if model_parameters.get('top_p'):
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req_params['top_p'] = model_parameters.get('top_p')
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if model_parameters.get('top_k'):
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req_params['top_k'] = model_parameters.get('top_k')
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if model_parameters.get('presence_penalty'):
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req_params['presence_penalty'] = model_parameters.get(
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'presence_penalty')
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if model_parameters.get('frequency_penalty'):
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req_params['frequency_penalty'] = model_parameters.get(
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'frequency_penalty')
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if stop:
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req_params['stop'] = stop
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req_params = get_v2_req_params(credentials, model_parameters, stop)
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extra_model_kwargs = {}
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if tools:
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extra_model_kwargs['tools'] = [
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MaaSClient.transform_tool_prompt_to_maas_config(tool) for tool in tools
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]
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resp = MaaSClient.wrap_exception(
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lambda: client.chat(req_params, prompt_messages, stream, **extra_model_kwargs))
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if not stream:
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@ -197,10 +175,8 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
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"""
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used to define customizable model schema
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"""
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max_tokens = ModelConfigs.get(
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credentials['base_model_name'], {}).get('req_params', {}).get('max_new_tokens')
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if credentials.get('max_tokens'):
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max_tokens = int(credentials.get('max_tokens'))
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model_config = get_model_config(credentials)
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rules = [
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ParameterRule(
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name='temperature',
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@ -234,10 +210,10 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
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name='presence_penalty',
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type=ParameterType.FLOAT,
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use_template='presence_penalty',
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label={
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'en_US': 'Presence Penalty',
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'zh_Hans': '存在惩罚',
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},
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label=I18nObject(
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en_US='Presence Penalty',
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zh_Hans= '存在惩罚',
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),
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min=-2.0,
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max=2.0,
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),
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@ -245,10 +221,10 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
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name='frequency_penalty',
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type=ParameterType.FLOAT,
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use_template='frequency_penalty',
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label={
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'en_US': 'Frequency Penalty',
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'zh_Hans': '频率惩罚',
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},
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label=I18nObject(
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en_US= 'Frequency Penalty',
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zh_Hans= '频率惩罚',
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),
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min=-2.0,
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max=2.0,
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),
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@ -257,7 +233,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
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type=ParameterType.INT,
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use_template='max_tokens',
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min=1,
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max=max_tokens,
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max=model_config.properties.max_tokens,
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default=512,
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label=I18nObject(
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zh_Hans='最大生成长度',
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@ -266,16 +242,9 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
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),
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]
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model_properties = ModelConfigs.get(
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credentials['base_model_name'], {}).get('model_properties', {}).copy()
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if credentials.get('mode'):
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model_properties[ModelPropertyKey.MODE] = credentials.get('mode')
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if credentials.get('context_size'):
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model_properties[ModelPropertyKey.CONTEXT_SIZE] = int(
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credentials.get('context_size', 4096))
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model_features = ModelConfigs.get(
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credentials['base_model_name'], {}).get('features', [])
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model_properties = {}
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model_properties[ModelPropertyKey.CONTEXT_SIZE] = model_config.properties.context_size
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model_properties[ModelPropertyKey.MODE] = model_config.properties.mode.value
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entity = AIModelEntity(
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model=model,
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@ -286,7 +255,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
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model_type=ModelType.LLM,
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model_properties=model_properties,
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parameter_rules=rules,
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features=model_features,
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features=model_config.features,
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)
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return entity
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@ -1,181 +1,123 @@
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from pydantic import BaseModel
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from core.model_runtime.entities.llm_entities import LLMMode
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from core.model_runtime.entities.model_entities import ModelFeature
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ModelConfigs = {
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'Doubao-pro-4k': {
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'req_params': {
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'max_prompt_tokens': 4096,
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'max_new_tokens': 4096,
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},
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'model_properties': {
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'context_size': 4096,
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'mode': 'chat',
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},
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'features': [
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ModelFeature.TOOL_CALL
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],
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},
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'Doubao-lite-4k': {
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'req_params': {
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'max_prompt_tokens': 4096,
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'max_new_tokens': 4096,
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},
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'model_properties': {
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'context_size': 4096,
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'mode': 'chat',
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},
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'features': [
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ModelFeature.TOOL_CALL
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],
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},
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'Doubao-pro-32k': {
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'req_params': {
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'max_prompt_tokens': 32768,
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'max_new_tokens': 32768,
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},
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'model_properties': {
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'context_size': 32768,
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'mode': 'chat',
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},
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'features': [
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ModelFeature.TOOL_CALL
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],
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},
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'Doubao-lite-32k': {
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'req_params': {
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'max_prompt_tokens': 32768,
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'max_new_tokens': 32768,
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},
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'model_properties': {
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'context_size': 32768,
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'mode': 'chat',
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},
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'features': [
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ModelFeature.TOOL_CALL
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],
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},
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'Doubao-pro-128k': {
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'req_params': {
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'max_prompt_tokens': 131072,
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'max_new_tokens': 131072,
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},
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'model_properties': {
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'context_size': 131072,
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'mode': 'chat',
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},
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'features': [
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ModelFeature.TOOL_CALL
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],
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},
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'Doubao-lite-128k': {
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'req_params': {
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'max_prompt_tokens': 131072,
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'max_new_tokens': 131072,
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},
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'model_properties': {
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'context_size': 131072,
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'mode': 'chat',
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},
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'features': [
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ModelFeature.TOOL_CALL
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],
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},
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'Skylark2-pro-4k': {
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'req_params': {
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'max_prompt_tokens': 4096,
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'max_new_tokens': 4000,
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},
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'model_properties': {
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'context_size': 4096,
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'mode': 'chat',
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},
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'features': [],
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},
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'Llama3-8B': {
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'req_params': {
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'max_prompt_tokens': 8192,
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'max_new_tokens': 8192,
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},
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'model_properties': {
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'context_size': 8192,
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'mode': 'chat',
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},
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'features': [],
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},
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'Llama3-70B': {
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'req_params': {
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'max_prompt_tokens': 8192,
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'max_new_tokens': 8192,
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},
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'model_properties': {
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'context_size': 8192,
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'mode': 'chat',
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},
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'features': [],
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},
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'Moonshot-v1-8k': {
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'req_params': {
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'max_prompt_tokens': 8192,
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'max_new_tokens': 4096,
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},
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'model_properties': {
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'context_size': 8192,
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'mode': 'chat',
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},
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'features': [],
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},
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'Moonshot-v1-32k': {
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'req_params': {
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'max_prompt_tokens': 32768,
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'max_new_tokens': 16384,
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},
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'model_properties': {
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'context_size': 32768,
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'mode': 'chat',
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},
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'features': [],
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},
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'Moonshot-v1-128k': {
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'req_params': {
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'max_prompt_tokens': 131072,
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'max_new_tokens': 65536,
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},
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'model_properties': {
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'context_size': 131072,
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'mode': 'chat',
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},
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'features': [],
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},
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'GLM3-130B': {
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'req_params': {
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'max_prompt_tokens': 8192,
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'max_new_tokens': 4096,
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},
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'model_properties': {
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'context_size': 8192,
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'mode': 'chat',
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},
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'features': [],
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},
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'GLM3-130B-Fin': {
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'req_params': {
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'max_prompt_tokens': 8192,
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'max_new_tokens': 4096,
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},
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'model_properties': {
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'context_size': 8192,
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'mode': 'chat',
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},
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'features': [],
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},
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'Mistral-7B': {
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'req_params': {
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'max_prompt_tokens': 8192,
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'max_new_tokens': 2048,
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},
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'model_properties': {
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'context_size': 8192,
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'mode': 'chat',
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},
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'features': [],
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}
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class ModelProperties(BaseModel):
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context_size: int
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max_tokens: int
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mode: LLMMode
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class ModelConfig(BaseModel):
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properties: ModelProperties
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features: list[ModelFeature]
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configs: dict[str, ModelConfig] = {
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'Doubao-pro-4k': ModelConfig(
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properties=ModelProperties(context_size=4096, max_tokens=4096, mode=LLMMode.CHAT),
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features=[ModelFeature.TOOL_CALL]
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),
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'Doubao-lite-4k': ModelConfig(
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properties=ModelProperties(context_size=4096, max_tokens=4096, mode=LLMMode.CHAT),
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features=[ModelFeature.TOOL_CALL]
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),
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'Doubao-pro-32k': ModelConfig(
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properties=ModelProperties(context_size=32768, max_tokens=32768, mode=LLMMode.CHAT),
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features=[ModelFeature.TOOL_CALL]
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),
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'Doubao-lite-32k': ModelConfig(
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properties=ModelProperties(context_size=32768, max_tokens=32768, mode=LLMMode.CHAT),
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features=[ModelFeature.TOOL_CALL]
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),
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'Doubao-pro-128k': ModelConfig(
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properties=ModelProperties(context_size=131072, max_tokens=131072, mode=LLMMode.CHAT),
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features=[ModelFeature.TOOL_CALL]
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),
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'Doubao-lite-128k': ModelConfig(
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properties=ModelProperties(context_size=131072, max_tokens=131072, mode=LLMMode.CHAT),
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features=[ModelFeature.TOOL_CALL]
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),
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'Skylark2-pro-4k': ModelConfig(
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properties=ModelProperties(context_size=4096, max_tokens=4000, mode=LLMMode.CHAT),
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features=[]
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),
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'Llama3-8B': ModelConfig(
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properties=ModelProperties(context_size=8192, max_tokens=8192, mode=LLMMode.CHAT),
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features=[]
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),
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'Llama3-70B': ModelConfig(
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properties=ModelProperties(context_size=8192, max_tokens=8192, mode=LLMMode.CHAT),
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features=[]
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),
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'Moonshot-v1-8k': ModelConfig(
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properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
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features=[]
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),
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'Moonshot-v1-32k': ModelConfig(
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properties=ModelProperties(context_size=32768, max_tokens=16384, mode=LLMMode.CHAT),
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features=[]
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),
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'Moonshot-v1-128k': ModelConfig(
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properties=ModelProperties(context_size=131072, max_tokens=65536, mode=LLMMode.CHAT),
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features=[]
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),
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'GLM3-130B': ModelConfig(
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properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
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features=[]
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),
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'GLM3-130B-Fin': ModelConfig(
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properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
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features=[]
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),
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'Mistral-7B': ModelConfig(
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properties=ModelProperties(context_size=8192, max_tokens=2048, mode=LLMMode.CHAT),
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features=[]
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)
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}
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def get_model_config(credentials: dict)->ModelConfig:
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base_model = credentials.get('base_model_name', '')
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model_configs = configs.get(base_model)
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if not model_configs:
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return ModelConfig(
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properties=ModelProperties(
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context_size=int(credentials.get('context_size', 0)),
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max_tokens=int(credentials.get('max_tokens', 0)),
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mode= LLMMode.value_of(credentials.get('mode', 'chat')),
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),
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features=[]
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)
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return model_configs
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def get_v2_req_params(credentials: dict, model_parameters: dict,
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stop: list[str] | None=None):
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req_params = {}
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# predefined properties
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model_configs = get_model_config(credentials)
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if model_configs:
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req_params['max_prompt_tokens'] = model_configs.properties.context_size
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req_params['max_new_tokens'] = model_configs.properties.max_tokens
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# model parameters
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if model_parameters.get('max_tokens'):
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req_params['max_new_tokens'] = model_parameters.get('max_tokens')
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if model_parameters.get('temperature'):
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req_params['temperature'] = model_parameters.get('temperature')
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if model_parameters.get('top_p'):
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req_params['top_p'] = model_parameters.get('top_p')
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if model_parameters.get('top_k'):
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req_params['top_k'] = model_parameters.get('top_k')
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if model_parameters.get('presence_penalty'):
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req_params['presence_penalty'] = model_parameters.get(
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'presence_penalty')
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if model_parameters.get('frequency_penalty'):
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req_params['frequency_penalty'] = model_parameters.get(
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'frequency_penalty')
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if stop:
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req_params['stop'] = stop
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return req_params
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@ -1,9 +1,27 @@
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from pydantic import BaseModel
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class ModelProperties(BaseModel):
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context_size: int
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max_chunks: int
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class ModelConfig(BaseModel):
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properties: ModelProperties
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ModelConfigs = {
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'Doubao-embedding': {
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'req_params': {},
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'model_properties': {
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'context_size': 4096,
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'max_chunks': 1,
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}
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},
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'Doubao-embedding': ModelConfig(
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properties=ModelProperties(context_size=4096, max_chunks=1)
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),
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}
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def get_model_config(credentials: dict)->ModelConfig:
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base_model = credentials.get('base_model_name', '')
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model_configs = ModelConfigs.get(base_model)
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if not model_configs:
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return ModelConfig(
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properties=ModelProperties(
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context_size=int(credentials.get('context_size', 0)),
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max_chunks=int(credentials.get('max_chunks', 0)),
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)
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)
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return model_configs
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@ -30,7 +30,7 @@ from core.model_runtime.model_providers.volcengine_maas.errors import (
|
|||
RateLimitErrors,
|
||||
ServerUnavailableErrors,
|
||||
)
|
||||
from core.model_runtime.model_providers.volcengine_maas.text_embedding.models import ModelConfigs
|
||||
from core.model_runtime.model_providers.volcengine_maas.text_embedding.models import get_model_config
|
||||
from core.model_runtime.model_providers.volcengine_maas.volc_sdk import MaasException
|
||||
|
||||
|
||||
|
|
@ -115,14 +115,10 @@ class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel):
|
|||
"""
|
||||
generate custom model entities from credentials
|
||||
"""
|
||||
model_properties = ModelConfigs.get(
|
||||
credentials['base_model_name'], {}).get('model_properties', {}).copy()
|
||||
if credentials.get('context_size'):
|
||||
model_properties[ModelPropertyKey.CONTEXT_SIZE] = int(
|
||||
credentials.get('context_size', 4096))
|
||||
if credentials.get('max_chunks'):
|
||||
model_properties[ModelPropertyKey.MAX_CHUNKS] = int(
|
||||
credentials.get('max_chunks', 4096))
|
||||
model_config = get_model_config(credentials)
|
||||
model_properties = {}
|
||||
model_properties[ModelPropertyKey.CONTEXT_SIZE] = model_config.properties.context_size
|
||||
model_properties[ModelPropertyKey.MAX_CHUNKS] = model_config.properties.max_chunks
|
||||
entity = AIModelEntity(
|
||||
model=model,
|
||||
label=I18nObject(en_US=model),
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user