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feat(model): Support Qwen2 models (#1612)
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Fangyin Cheng
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@ -154,6 +154,11 @@ DB-GPTのアーキテクチャは以下の図に示されています：
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私たちは、LLaMA/LLaMA2、Baichuan、ChatGLM、Wenxin、Tongyi、Zhipuなど、オープンソースおよびAPIエージェントからの数十の大規模言語モデル（LLM）を含む幅広いモデルをサポートしています。
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- ニュース
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- 🔥🔥🔥 [Qwen2-57B-A14B-Instruct](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct)
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- 🔥🔥🔥 [Qwen2-72B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct)
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- 🔥🔥🔥 [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)
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- 🔥🔥🔥 [Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct)
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- 🔥🔥🔥 [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct)
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- 🔥🔥🔥 [glm-4-9b-chat](https://huggingface.co/THUDM/glm-4-9b-chat)
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- 🔥🔥🔥 [Phi-3](https://huggingface.co/collections/microsoft/phi-3-6626e15e9585a200d2d761e3)
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- 🔥🔥🔥 [Yi-1.5-34B-Chat](https://huggingface.co/01-ai/Yi-1.5-34B-Chat)
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@ -158,6 +158,11 @@ At present, we have introduced several key features to showcase our current capa
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We offer extensive model support, including dozens of large language models (LLMs) from both open-source and API agents, such as LLaMA/LLaMA2, Baichuan, ChatGLM, Wenxin, Tongyi, Zhipu, and many more.
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- News
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- 🔥🔥🔥 [Qwen2-57B-A14B-Instruct](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct)
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- 🔥🔥🔥 [Qwen2-72B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct)
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- 🔥🔥🔥 [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)
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- 🔥🔥🔥 [Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct)
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- 🔥🔥🔥 [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct)
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- 🔥🔥🔥 [glm-4-9b-chat](https://huggingface.co/THUDM/glm-4-9b-chat)
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- 🔥🔥🔥 [Phi-3](https://huggingface.co/collections/microsoft/phi-3-6626e15e9585a200d2d761e3)
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- 🔥🔥🔥 [Yi-1.5-34B-Chat](https://huggingface.co/01-ai/Yi-1.5-34B-Chat)
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@ -152,6 +152,11 @@
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海量模型支持，包括开源、API代理等几十种大语言模型。如LLaMA/LLaMA2、Baichuan、ChatGLM、文心、通义、智谱等。当前已支持如下模型:
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- 新增支持模型
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- 🔥🔥🔥 [Qwen2-57B-A14B-Instruct](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct)
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- 🔥🔥🔥 [Qwen2-72B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct)
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- 🔥🔥🔥 [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)
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- 🔥🔥🔥 [Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct)
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- 🔥🔥🔥 [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct)
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- 🔥🔥🔥 [glm-4-9b-chat](https://huggingface.co/THUDM/glm-4-9b-chat)
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- 🔥🔥🔥 [Phi-3](https://huggingface.co/collections/microsoft/phi-3-6626e15e9585a200d2d761e3)
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- 🔥🔥🔥 [Yi-1.5-34B-Chat](https://huggingface.co/01-ai/Yi-1.5-34B-Chat)
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@ -54,6 +54,7 @@ LLM_MODEL_CONFIG = {
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"chatglm3-6b": os.path.join(MODEL_PATH, "chatglm3-6b"),
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# https://huggingface.co/THUDM/glm-4-9b-chat
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"glm-4-9b-chat": os.path.join(MODEL_PATH, "glm-4-9b-chat"),
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"glm-4-9b-chat-1m": os.path.join(MODEL_PATH, "glm-4-9b-chat-1m"),
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"guanaco-33b-merged": os.path.join(MODEL_PATH, "guanaco-33b-merged"),
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"falcon-40b": os.path.join(MODEL_PATH, "falcon-40b"),
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"gorilla-7b": os.path.join(MODEL_PATH, "gorilla-7b"),
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@ -124,6 +125,42 @@ LLM_MODEL_CONFIG = {
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"codeqwen1.5-7b-chat": os.path.join(MODEL_PATH, "CodeQwen1.5-7B-Chat"),
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# https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B-Chat
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"qwen1.5-moe-a2.7b-chat": os.path.join(MODEL_PATH, "Qwen1.5-MoE-A2.7B-Chat"),
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"qwen2-57b-a14b-instruct": os.path.join(MODEL_PATH, "Qwen2-57B-A14B-Instruct"),
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"qwen2-57b-a14b-instruct-gptq-int4": os.path.join(
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MODEL_PATH, "Qwen2-57B-A14B-Instruct-GPTQ-Int4"
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),
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"qwen2-72b-instruct": os.path.join(MODEL_PATH, "Qwen2-72B-Instruct"),
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"qwen2-72b-instruct-awq": os.path.join(MODEL_PATH, "Qwen2-72B-Instruct-AWQ"),
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"qwen2-72b-instruct-gptq-int8": os.path.join(
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MODEL_PATH, "Qwen2-72B-Instruct-GPTQ-Int8"
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),
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"qwen2-72b-instruct-gptq-int4": os.path.join(
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MODEL_PATH, "Qwen2-72B-Instruct-GPTQ-Int4"
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),
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"qwen2-7b-instruct": os.path.join(MODEL_PATH, "Qwen2-7B-Instruct"),
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"qwen2-7b-instruct-awq": os.path.join(MODEL_PATH, "Qwen2-7B-Instruct-AWQ"),
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"qwen2-7b-instruct-gptq-int8": os.path.join(
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MODEL_PATH, "Qwen2-7B-Instruct-GPTQ-Int8"
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),
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"qwen2-7b-instruct-gptq-int4": os.path.join(
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MODEL_PATH, "Qwen2-7B-Instruct-GPTQ-Int4"
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),
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"qwen2-1.5b-instruct": os.path.join(MODEL_PATH, "Qwen2-1.5B-Instruct"),
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"qwen2-1.5b-instruct-awq": os.path.join(MODEL_PATH, "Qwen2-1.5B-Instruct-AWQ"),
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"qwen2-1.5b-instruct-gptq-int8": os.path.join(
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MODEL_PATH, "Qwen2-1.5B-Instruct-GPTQ-Int8"
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),
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"qwen2-1.5b-instruct-gptq-int4": os.path.join(
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MODEL_PATH, "Qwen2-1.5B-Instruct-GPTQ-Int4"
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),
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"qwen2-0.5b-instruct": os.path.join(MODEL_PATH, "Qwen2-0.5B-Instruct"),
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"qwen2-0.5b-instruct-awq": os.path.join(MODEL_PATH, "Qwen2-0.5B-Instruct-AWQ"),
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"qwen2-0.5b-instruct-gptq-int8": os.path.join(
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MODEL_PATH, "Qwen2-0.5B-Instruct-GPTQ-Int8"
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),
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"qwen2-0.5b-instruct-gptq-int4": os.path.join(
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MODEL_PATH, "Qwen2-0.5B-Instruct-GPTQ-Int4"
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),
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# (Llama2 based) We only support WizardLM-13B-V1.2 for now, which is trained from Llama-2 13b, see https://huggingface.co/WizardLM/WizardLM-13B-V1.2
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"wizardlm-13b": os.path.join(MODEL_PATH, "WizardLM-13B-V1.2"),
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# wget https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF/resolve/main/vicuna-13b-v1.5.Q4_K_M.gguf -O models/ggml-model-q4_0.gguf
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@ -310,6 +310,20 @@ class QwenAdapter(NewHFChatModelAdapter):
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and "qwen" in lower_model_name_or_path
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and "1.5" in lower_model_name_or_path
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and "moe" not in lower_model_name_or_path
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and "qwen2" not in lower_model_name_or_path
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)
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class Qwen2Adapter(QwenAdapter):
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support_4bit: bool = True
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support_8bit: bool = True
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def do_match(self, lower_model_name_or_path: Optional[str] = None):
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return (
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lower_model_name_or_path
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and "qwen2" in lower_model_name_or_path
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and "instruct" in lower_model_name_or_path
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)
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@ -517,3 +531,4 @@ register_model_adapter(PhiAdapter)
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register_model_adapter(SQLCoderAdapter)
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register_model_adapter(OpenChatAdapter)
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register_model_adapter(GLM4Aapter)
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register_model_adapter(Qwen2Adapter)
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