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1. 修改硅基智能模型的名称格式，要求添加"siliconflow-"前缀。 

2. 修改硅基智能模型的请求代码：
2.1. 根据文档，提高max_output_token和模型的max_token。
2.2. 简化模型列表，要求自己添加模型。
2.3. 添加支持推理能力的模型。
This commit is contained in:
Samon Yu 2025-04-17 20:16:45 +08:00
parent 406abd4fd7
commit c94839f581
2 changed files with 36 additions and 58 deletions
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12
config.py
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@ -48,8 +48,8 @@ AVAIL_LLM_MODELS = ["qwen-max", "o1-mini", "o1-mini-2024-09-12", "o1", "o1-2024-
"gemini-1.5-pro", "chatglm3", "chatglm4", "gemini-1.5-pro", "chatglm3", "chatglm4",
"deepseek-chat", "deepseek-coder", "deepseek-reasoner", "deepseek-chat", "deepseek-coder", "deepseek-reasoner",
"deepseek-ai/DeepSeek-R1","deepseek-ai/DeepSeek-V3", "siliconflow-deepseek-ai/DeepSeek-R1","siliconflow-deepseek-ai/DeepSeek-V3", "siliconflow-Qwen/QwQ-32B",
"Qwen/Qwen2.5-32B-Instruct","Qwen/Qwen2.5-14B-Instruct","Qwen/Qwen2.5-7B-Instruct", "siliconflow-Qwen/Qwen2.5-32B-Instruct","siliconflow-Qwen/Qwen2.5-14B-Instruct"
"volcengine-deepseek-r1-250120", "volcengine-deepseek-v3-241226", "volcengine-deepseek-r1-250120", "volcengine-deepseek-v3-241226",
"dashscope-deepseek-r1", "dashscope-deepseek-v3", "dashscope-deepseek-r1", "dashscope-deepseek-v3",
] ]
@ -77,10 +77,12 @@ EMBEDDING_MODEL = "text-embedding-3-small"
# 使用"one-api-*","vllm-*","ollama-*","openrouter-*"前缀直接使用非标准方式接入的模型，例如 # 使用"one-api-*","vllm-*","ollama-*","openrouter-*"前缀直接使用非标准方式接入的模型，例如
# AVAIL_LLM_MODELS = ["one-api-claude-3-sonnet-20240229(max_token=100000)", "ollama-phi3(max_token=4096)","openrouter-openai/gpt-4o-mini","openrouter-openai/chatgpt-4o-latest"] # AVAIL_LLM_MODELS = ["one-api-claude-3-sonnet-20240229(max_token=100000)", "ollama-phi3(max_token=4096)","openrouter-openai/gpt-4o-mini","openrouter-openai/chatgpt-4o-latest"]
# --- --- --- --- # --- --- --- ---
# 硅基智能(Siliconflow) API 可以通过 https://cloud.siliconflow.cn/models 或 https://docs.siliconflow.cn/api-reference/chat-completions/chat-completions 获取模型名称，并放置在AVAIL_LLM_MODELS列表中。 # 硅基智能(Siliconflow) API 可以通过 https://cloud.siliconflow.cn/models 或 https://docs.siliconflow.cn/api-reference/chat-completions/chat-completions 获取模型名称
# 在模型名称前添加"siliconflow-"前缀，并放置在AVAIL_LLM_MODELS列表中。
# AVAIL_LLM_MODELS = [ # AVAIL_LLM_MODELS = [
# "deepseek-ai/DeepSeek-R1","deepseek-ai/DeepSeek-V3", # "siliconflow-deepseek-ai/DeepSeek-R1","siliconflow-deepseek-ai/DeepSeek-V3", "siliconflow-Qwen/QwQ-32B",
# "Qwen/Qwen2.5-32B-Instruct","Qwen/Qwen2.5-14B-Instruct","Qwen/Qwen2.5-7B-Instruct" # "siliconflow-Qwen/Qwen2.5-32B-Instruct","siliconflow-Qwen/Qwen2.5-14B-Instruct","siliconflow-Qwen/Qwen2.5-7B-Instruct",
# "siliconflow-THUDM/GLM-Z1-32B-0414"
# ] # ]
# --- --- --- --- # --- --- --- ---

58
request_llms/bridge_all.py
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@ -1343,62 +1343,38 @@ for model in [m for m in AVAIL_LLM_MODELS if m.startswith("openrouter-")]:
# -=-=-=-=-=-=- 硅基智能SiliconFlow在线API -=-=-=-=-=-=- # -=-=-=-=-=-=- 硅基智能SiliconFlow在线API -=-=-=-=-=-=-
siliconflow_models = [ # 支持推理的模型系列，非完整模型名称
"deepseek-ai/DeepSeek-R1", "Pro/deepseek-ai/DeepSeek-R1", "deepseek-ai/DeepSeek-V3", "Pro/deepseek-ai/DeepSeek-V3", inference_model_series = [
"deepseek-ai/DeepSeek-R1-Distill-Llama-70B", "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "THUDM/GLM-Z1", "deepseek-ai/DeepSeek-R1", "Qwen/QwQ-32B"
"deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"Pro/deepseek-ai/DeepSeek-R1-Distill-Llama-8B","Pro/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B","Pro/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"meta-llama/Llama-3.3-70B-Instruct","AIDC-AI/Marco-o1","deepseek-ai/DeepSeek-V2.5",
"Qwen/Qwen2.5-72B-Instruct-128K","Qwen/Qwen2.5-72B-Instruct","Qwen/Qwen2.5-32B-Instruct","Qwen/Qwen2.5-14B-Instruct","Qwen/Qwen2.5-7B-Instruct",
"Qwen/Qwen2.5-Coder-32B-Instruct","Qwen/Qwen2.5-Coder-7B-Instruct",
"Qwen/Qwen2-7B-Instruct","Qwen/Qwen2-1.5B-Instruct","Qwen/QwQ-32B-Preview",
"TeleAI/TeleChat2",
"01-ai/Yi-1.5-34B-Chat-16K","01-ai/Yi-1.5-9B-Chat-16K","01-ai/Yi-1.5-6B-Chat",
"THUDM/glm-4-9b-chat","Vendor-A/Qwen/Qwen2.5-72B-Instruct",
"internlm/internlm2_5-7b-chat","internlm/internlm2_5-20b-chat",
"nvidia/Llama-3.1-Nemotron-70B-Instruct",
"meta-llama/Meta-Llama-3.1-405B-Instruct","meta-llama/Meta-Llama-3.1-70B-Instruct","meta-llama/Meta-Llama-3.1-8B-Instruct",
"google/gemma-2-27b-it","google/gemma-2-9b-it",
"Pro/Qwen/Qwen2.5-7B-Instruct","Pro/Qwen/Qwen2-7B-Instruct","Pro/Qwen/Qwen2-1.5B-Instruct",
"Pro/THUDM/chatglm3-6b","Pro/THUDM/glm-4-9b-chat",
"Pro/meta-llama/Meta-Llama-3.1-8B-Instruct",
"Pro/google/gemma-2-9b-it",
] ]
for model in [m for m in AVAIL_LLM_MODELS if m.startswith("siliconflow-")]:
# 模型名称的格式为：
# "siliconflow-<model_name>"
# 其中：
# "siliconflow-" 是前缀（必要），在实际请求中，会将其去掉。
# "deepseek-ai/DeepSeek-R1" 是硅基智能提供的模型名（必要）。
if any(item in siliconflow_models for item in AVAIL_LLM_MODELS):
try: try:
siliconflow_noui, siliconflow_ui = get_predict_function( siliconflow_noui, siliconflow_ui = get_predict_function(
api_key_conf_name="SILICONFLOW_API_KEY", api_key_conf_name="SILICONFLOW_API_KEY",
max_output_token=4096, max_output_token=8192,
disable_proxy=False, disable_proxy=False,
# 去除前缀
model_remove_prefix = ["siliconflow-"]
) )
for item in (set(siliconflow_models) & set(AVAIL_LLM_MODELS)): # 判断是否具有推理能力
if "DeepSeek-R1" in item: enable_reasoning = any(item in model for item in inference_model_series)
model_info.update( model_info.update(
{ {
item: { model: {
"fn_with_ui": siliconflow_ui, "fn_with_ui": siliconflow_ui,
"fn_without_ui": siliconflow_noui, "fn_without_ui": siliconflow_noui,
"endpoint": siliconflow_endpoint, "endpoint": siliconflow_endpoint,
"can_multi_thread": True, "can_multi_thread": True,
"max_token": 8000, "max_token": 32000,
"tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35,
"enable_reasoning": True,
},
}
)
else:
model_info.update(
{
item: {
"fn_with_ui": siliconflow_ui,
"fn_without_ui": siliconflow_noui,
"endpoint": siliconflow_endpoint,
"can_multi_thread": True,
"max_token": 8000,
"tokenizer": tokenizer_gpt35, "tokenizer": tokenizer_gpt35,
"token_cnt": get_token_num_gpt35, "token_cnt": get_token_num_gpt35,
"enable_reasoning": enable_reasoning,
}, },
} }
) )