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add support for NVIDIA llm (#1645)
### What problem does this PR solve? add support for NVIDIA llm ### Type of change - [x] New Feature (non-breaking change which adds functionality) --------- Co-authored-by: Zhedong Cen <cenzhedong2@126.com>
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@ -1918,6 +1918,290 @@
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"model_type": "chat"
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}
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]
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},
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{
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"name": "NVIDIA",
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"logo": "",
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"tags": "LLM,TEXT EMBEDDING, TEXT RE-RANK",
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"status": "1",
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"llm": [
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{
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"llm_name": "nvidia/nemotron-4-340b-reward",
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"tags": "LLM,CHAT,4K",
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"max_tokens": 4096,
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"model_type": "chat"
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},
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{
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"llm_name": "aisingapore/sea-lion-7b-instruct",
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"tags": "LLM,CHAT,4K",
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"max_tokens": 4096,
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"model_type": "chat"
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},
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{
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"llm_name": "databricks/dbrx-instruct",
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"tags": "LLM,CHAT,16K",
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"max_tokens": 16384,
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"model_type": "chat"
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},
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{
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"llm_name": "google/gemma-7b",
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"tags": "LLM,CHAT,32K",
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"max_tokens": 32768,
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"model_type": "chat"
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},
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{
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"llm_name": "google/gemma-2b",
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"tags": "LLM,CHAT,16K",
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"max_tokens": 16384,
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"model_type": "chat"
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},
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{
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"llm_name": "google/gemma-2-9b-it",
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"tags": "LLM,CHAT,8K",
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"max_tokens": 8192,
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"model_type": "chat"
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},
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{
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"llm_name": "google/gemma-2-27b-it",
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"tags": "LLM,CHAT,8K",
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"max_tokens": 8192,
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"model_type": "chat"
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},
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{
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"llm_name": "google/recurrentgemma-2b",
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"tags": "LLM,CHAT,4K",
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"max_tokens": 4096,
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"model_type": "chat"
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},
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{
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"llm_name": "mediatek/breeze-7b-instruct",
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"tags": "LLM,CHAT,8K",
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"max_tokens": 8192,
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"model_type": "chat"
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},
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{
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"llm_name": "meta/llama2-70b",
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"tags": "LLM,CHAT,4K",
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"max_tokens": 4096,
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"model_type": "chat"
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},
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{
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"llm_name": "meta/llama3-8b",
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"tags": "LLM,CHAT,8K",
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"max_tokens": 8192,
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"model_type": "chat"
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},
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{
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"llm_name": "meta/llama3-70b",
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"tags": "LLM,CHAT,8K",
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"max_tokens": 8192,
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"model_type": "chat"
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},
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{
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"llm_name": "microsoft/phi-3-medium-128k-instruct",
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"tags": "LLM,CHAT,128K",
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"max_tokens": 131072,
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"model_type": "chat"
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},
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{
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"llm_name": "microsoft/phi-3-medium-4k-instruct",
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"tags": "LLM,CHAT,4K",
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"max_tokens": 4096,
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"model_type": "chat"
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},
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{
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"llm_name": "microsoftphi-3-mini-128k-instruct",
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"tags": "LLM,CHAT,128K",
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"max_tokens": 131072,
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"model_type": "chat"
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},
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{
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"llm_name": "microsoft/phi-3-mini-4k-instruct",
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"tags": "LLM,CHAT,4K",
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"max_tokens": 4096,
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"model_type": "chat"
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},
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{
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"llm_name": "microsoft/phi-3-small-128k-instruct",
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"tags": "LLM,CHAT,128K",
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"max_tokens": 131072,
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"model_type": "chat"
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},
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{
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"llm_name": "microsoft/phi-3-small-8k-instruct",
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"tags": "LLM,CHAT,8K",
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"max_tokens": 8192,
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"model_type": "chat"
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},
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{
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"llm_name": "mistralai/mistral-7b-instruct",
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"tags": "LLM,CHAT,4K",
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"max_tokens": 4096,
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"model_type": "chat"
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},
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{
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"llm_name": "mistralai/mistral-7b-instruct-v0.3",
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"tags": "LLM,CHAT,4K",
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"max_tokens": 4096,
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"model_type": "chat"
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},
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{
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"llm_name": "mistralai/mixtral-8x7b-instruct",
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"tags": "LLM,CHAT,32K",
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"max_tokens": 32768,
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"model_type": "chat"
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},
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{
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"llm_name": "mistralai/mixtral-8x22b-instruct",
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"tags": "LLM,CHAT,64K",
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"max_tokens": 65536,
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"model_type": "chat"
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},
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{
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"llm_name": "mistralai/mistral-large",
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"tags": "LLM,CHAT,32K",
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"max_tokens": 32768,
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"model_type": "chat"
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},
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{
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"llm_name": "nv-mistralai/mistral-nemo-12b-instruct",
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"tags": "LLM,CHAT,128K",
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"max_tokens": 131072,
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"model_type": "chat"
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},
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{
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"llm_name": "nvidia/llama3-chatqa-1.5-70b",
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"tags": "LLM,CHAT,4K",
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"max_tokens": 4096,
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"model_type": "chat"
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},
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{
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"llm_name": "nvidia/llama3-chatqa-1.5-8b",
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"tags": "LLM,CHAT,4K",
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"max_tokens": 4096,
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"model_type": "chat"
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},
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{
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"llm_name": "nvidia/nemotron-4-340b-instruct",
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"tags": "LLM,CHAT,4K",
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"max_tokens": 4096,
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"model_type": "chat"
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},
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{
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"llm_name": "seallms/seallm-7b-v2.5",
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"tags": "LLM,CHAT,4K",
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"max_tokens": 4096,
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"model_type": "chat"
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},
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{
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"llm_name": "snowflake/arctic",
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"tags": "LLM,CHAT,4K",
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"max_tokens": 4096,
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"model_type": "chat"
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},
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{
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"llm_name": "upstage/solar-10.7b-instruct",
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"tags": "LLM,CHAT,4K",
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"max_tokens": 4096,
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"model_type": "chat"
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},
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{
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"llm_name": "baai/bge-m3",
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"tags": "TEXT EMBEDDING,8K",
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"max_tokens": 8192,
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"model_type": "embedding"
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},
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{
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"llm_name": "nvidia/embed-qa-4",
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"tags": "TEXT EMBEDDING,512",
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"max_tokens": 512,
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"model_type": "embedding"
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},
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{
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"llm_name": "nvidia/nv-embed-v1",
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"tags": "TEXT EMBEDDING,32K",
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"max_tokens": 32768,
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"model_type": "embedding"
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},
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{
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"llm_name": "nvidia/nv-embedqa-e5-v5",
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"tags": "TEXT EMBEDDING,512",
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"max_tokens": 512,
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"model_type": "embedding"
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},
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{
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"llm_name": "nvidia/nv-embedqa-mistral-7b-v2",
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"tags": "TEXT EMBEDDING,512",
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"max_tokens": 512,
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"model_type": "embedding"
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},
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{
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"llm_name": "nvidia/nv-rerankqa-mistral-4b-v3",
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"tags": "RE-RANK,512",
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"max_tokens": 512,
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"model_type": "rerank"
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},
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{
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"llm_name": "nvidia/rerank-qa-mistral-4b",
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"tags": "RE-RANK,512",
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"max_tokens": 512,
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"model_type": "rerank"
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},
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{
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"llm_name": "snowflake/arctic-embed-l",
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"tags": "TEXT EMBEDDING,512",
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"max_tokens": 512,
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"model_type": "embedding"
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},
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{
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"llm_name": "adept/fuyu-8b",
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"tags": "LLM,IMAGE2TEXT,4K",
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"max_tokens": 4096,
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"model_type": "image2text"
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},
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{
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"llm_name": "google/deplot",
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"tags": "LLM,IMAGE2TEXT,4K",
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"max_tokens": 4096,
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"model_type": "image2text"
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},
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{
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"llm_name": "google/paligemma",
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"tags": "LLM,IMAGE2TEXT,4K",
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"max_tokens": 4096,
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"model_type": "image2text"
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},
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{
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"llm_name": "Iiuhaotian/Ilava-v1.6-34b",
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"tags": "LLM,IMAGE2TEXT,4K",
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"max_tokens": 4096,
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"model_type": "image2text"
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},
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{
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"llm_name": "Iiuhaotian/Ilava-v1.6-mistral-7b",
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"tags": "LLM,IMAGE2TEXT,4K",
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"max_tokens": 4096,
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"model_type": "image2text"
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},
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{
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"llm_name": "microsoft/kosmos-2",
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"tags": "LLM,IMAGE2TEXT,4K",
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"max_tokens": 4096,
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"model_type": "image2text"
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},
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{
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"llm_name": "microsoft/phi-3-vision-128k-instruct",
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"tags": "LLM,IMAGE2TEXT,128K",
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"max_tokens": 131072,
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"model_type": "image2text"
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},
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{
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"llm_name": "nvidia/neva-22b",
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"tags": "LLM,IMAGE2TEXT,4K",
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"max_tokens": 4096,
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"model_type": "image2text"
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}
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]
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}
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]
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}
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@ -34,7 +34,8 @@ EmbeddingModel = {
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"BAAI": DefaultEmbedding,
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"Mistral": MistralEmbed,
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"Bedrock": BedrockEmbed,
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"Gemini":GeminiEmbed
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"Gemini":GeminiEmbed,
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"NVIDIA":NvidiaEmbed
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}
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@ -48,7 +49,8 @@ CvModel = {
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"Moonshot": LocalCV,
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'Gemini':GeminiCV,
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'OpenRouter':OpenRouterCV,
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"LocalAI":LocalAICV
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"LocalAI":LocalAICV,
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"NVIDIA":NvidiaCV
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}
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@ -71,7 +73,8 @@ ChatModel = {
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"Bedrock": BedrockChat,
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"Groq": GroqChat,
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'OpenRouter':OpenRouterChat,
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"StepFun":StepFunChat
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"StepFun":StepFunChat,
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"NVIDIA":NvidiaChat
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}
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@ -79,7 +82,8 @@ RerankModel = {
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"BAAI": DefaultRerank,
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"Jina": JinaRerank,
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"Youdao": YoudaoRerank,
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"Xinference": XInferenceRerank
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"Xinference": XInferenceRerank,
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"NVIDIA":NvidiaRerank
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}
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@ -581,7 +581,6 @@ class MiniMaxChat(Base):
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response = requests.request(
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"POST", url=self.base_url, headers=headers, data=payload
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)
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print(response, flush=True)
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response = response.json()
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ans = response["choices"][0]["message"]["content"].strip()
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if response["choices"][0]["finish_reason"] == "length":
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@ -902,4 +901,79 @@ class StepFunChat(Base):
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def __init__(self, key, model_name, base_url="https://api.stepfun.com/v1/chat/completions"):
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if not base_url:
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base_url = "https://api.stepfun.com/v1/chat/completions"
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super().__init__(key, model_name, base_url)
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super().__init__(key, model_name, base_url)
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class NvidiaChat(Base):
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def __init__(
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self,
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key,
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model_name,
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base_url="https://integrate.api.nvidia.com/v1/chat/completions",
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):
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if not base_url:
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base_url = "https://integrate.api.nvidia.com/v1/chat/completions"
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self.base_url = base_url
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self.model_name = model_name
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self.api_key = key
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self.headers = {
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"accept": "application/json",
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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}
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def chat(self, system, history, gen_conf):
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if system:
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history.insert(0, {"role": "system", "content": system})
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for k in list(gen_conf.keys()):
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if k not in ["temperature", "top_p", "max_tokens"]:
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del gen_conf[k]
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payload = {"model": self.model_name, "messages": history, **gen_conf}
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try:
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response = requests.post(
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url=self.base_url, headers=self.headers, json=payload
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)
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response = response.json()
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ans = response["choices"][0]["message"]["content"].strip()
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return ans, response["usage"]["total_tokens"]
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except Exception as e:
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return "**ERROR**: " + str(e), 0
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def chat_streamly(self, system, history, gen_conf):
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if system:
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history.insert(0, {"role": "system", "content": system})
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for k in list(gen_conf.keys()):
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if k not in ["temperature", "top_p", "max_tokens"]:
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del gen_conf[k]
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ans = ""
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total_tokens = 0
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payload = {
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"model": self.model_name,
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"messages": history,
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"stream": True,
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**gen_conf,
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}
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try:
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response = requests.post(
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url=self.base_url,
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headers=self.headers,
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json=payload,
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)
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for resp in response.text.split("\n\n"):
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if "choices" not in resp:
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continue
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resp = json.loads(resp[6:])
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if "content" in resp["choices"][0]["delta"]:
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text = resp["choices"][0]["delta"]["content"]
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else:
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continue
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ans += text
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if "usage" in resp:
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total_tokens = resp["usage"]["total_tokens"]
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yield ans
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except Exception as e:
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yield ans + "\n**ERROR**: " + str(e)
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yield total_tokens
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@ -137,7 +137,6 @@ class Base(ABC):
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]
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class GptV4(Base):
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def __init__(self, key, model_name="gpt-4-vision-preview", lang="Chinese", base_url="https://api.openai.com/v1"):
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if not base_url: base_url="https://api.openai.com/v1"
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@ -619,3 +618,65 @@ class LocalCV(Base):
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def describe(self, image, max_tokens=1024):
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return "", 0
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class NvidiaCV(Base):
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def __init__(
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self,
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key,
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model_name,
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lang="Chinese",
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base_url="https://ai.api.nvidia.com/v1/vlm",
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):
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if not base_url:
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base_url = ("https://ai.api.nvidia.com/v1/vlm",)
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self.lang = lang
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factory, llm_name = model_name.split("/")
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if factory != "liuhaotian":
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self.base_url = os.path.join(base_url, factory, llm_name)
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else:
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self.base_url = os.path.join(
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base_url, "community", llm_name.replace("-v1.6", "16")
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)
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self.key = key
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def describe(self, image, max_tokens=1024):
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b64 = self.image2base64(image)
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response = requests.post(
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url=self.base_url,
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headers={
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"accept": "application/json",
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"content-type": "application/json",
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"Authorization": f"Bearer {self.key}",
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},
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json={
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"messages": self.prompt(b64),
|
||||
"max_tokens": max_tokens,
|
||||
},
|
||||
)
|
||||
response = response.json()
|
||||
return (
|
||||
response["choices"][0]["message"]["content"].strip(),
|
||||
response["usage"]["total_tokens"],
|
||||
)
|
||||
|
||||
def prompt(self, b64):
|
||||
return [
|
||||
{
|
||||
"role": "user",
|
||||
"content": (
|
||||
"请用中文详细描述一下图中的内容，比如时间，地点，人物，事情，人物心情等，如果有数据请提取出数据。"
|
||||
if self.lang.lower() == "chinese"
|
||||
else "Please describe the content of this picture, like where, when, who, what happen. If it has number data, please extract them out."
|
||||
)
|
||||
+ f' <img src="data:image/jpeg;base64,{b64}"/>',
|
||||
}
|
||||
]
|
||||
|
||||
def chat_prompt(self, text, b64):
|
||||
return [
|
||||
{
|
||||
"role": "user",
|
||||
"content": text + f' <img src="data:image/jpeg;base64,{b64}"/>',
|
||||
}
|
||||
]
|
||||
|
||||
@ -462,3 +462,41 @@ class GeminiEmbed(Base):
|
||||
title="Embedding of single string")
|
||||
token_count = num_tokens_from_string(text)
|
||||
return np.array(result['embedding']),token_count
|
||||
|
||||
class NvidiaEmbed(Base):
|
||||
def __init__(
|
||||
self, key, model_name, base_url="https://integrate.api.nvidia.com/v1/embeddings"
|
||||
):
|
||||
if not base_url:
|
||||
base_url = "https://integrate.api.nvidia.com/v1/embeddings"
|
||||
self.api_key = key
|
||||
self.base_url = base_url
|
||||
self.headers = {
|
||||
"accept": "application/json",
|
||||
"Content-Type": "application/json",
|
||||
"authorization": f"Bearer {self.api_key}",
|
||||
}
|
||||
self.model_name = model_name
|
||||
if model_name == "nvidia/embed-qa-4":
|
||||
self.base_url = "https://ai.api.nvidia.com/v1/retrieval/nvidia/embeddings"
|
||||
self.model_name = "NV-Embed-QA"
|
||||
if model_name == "snowflake/arctic-embed-l":
|
||||
self.base_url = "https://ai.api.nvidia.com/v1/retrieval/snowflake/arctic-embed-l/embeddings"
|
||||
|
||||
def encode(self, texts: list, batch_size=None):
|
||||
payload = {
|
||||
"input": texts,
|
||||
"input_type": "query",
|
||||
"model": self.model_name,
|
||||
"encoding_format": "float",
|
||||
"truncate": "END",
|
||||
}
|
||||
res = requests.post(self.base_url, headers=self.headers, json=payload).json()
|
||||
return (
|
||||
np.array([d["embedding"] for d in res["data"]]),
|
||||
res["usage"]["total_tokens"],
|
||||
)
|
||||
|
||||
def encode_queries(self, text):
|
||||
embds, cnt = self.encode([text])
|
||||
return np.array(embds[0]), cnt
|
||||
|
||||
@ -164,3 +164,41 @@ class LocalAIRerank(Base):
|
||||
|
||||
def similarity(self, query: str, texts: list):
|
||||
raise NotImplementedError("The LocalAIRerank has not been implement")
|
||||
|
||||
|
||||
class NvidiaRerank(Base):
|
||||
def __init__(
|
||||
self, key, model_name, base_url="https://ai.api.nvidia.com/v1/retrieval/nvidia/"
|
||||
):
|
||||
if not base_url:
|
||||
base_url = "https://ai.api.nvidia.com/v1/retrieval/nvidia/"
|
||||
self.model_name = model_name
|
||||
|
||||
if self.model_name == "nvidia/nv-rerankqa-mistral-4b-v3":
|
||||
self.base_url = os.path.join(
|
||||
base_url, "nv-rerankqa-mistral-4b-v3", "reranking"
|
||||
)
|
||||
|
||||
if self.model_name == "nvidia/rerank-qa-mistral-4b":
|
||||
self.base_url = os.path.join(base_url, "reranking")
|
||||
self.model_name = "nv-rerank-qa-mistral-4b:1"
|
||||
|
||||
self.headers = {
|
||||
"accept": "application/json",
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {key}",
|
||||
}
|
||||
|
||||
def similarity(self, query: str, texts: list):
|
||||
token_count = num_tokens_from_string(query) + sum(
|
||||
[num_tokens_from_string(t) for t in texts]
|
||||
)
|
||||
data = {
|
||||
"model": self.model_name,
|
||||
"query": {"text": query},
|
||||
"passages": [{"text": text} for text in texts],
|
||||
"truncate": "END",
|
||||
"top_n": len(texts),
|
||||
}
|
||||
res = requests.post(self.base_url, headers=self.headers, json=data).json()
|
||||
return (np.array([d["logit"] for d in res["rankings"]]), token_count)
|
||||
|
||||
1
web/src/assets/svg/llm/nvidia.svg
Normal file
1
web/src/assets/svg/llm/nvidia.svg
Normal file
@ -0,0 +1 @@
|
||||
<?xml version="1.0" standalone="no"?><!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd"><svg t="1721640561969" class="icon" viewBox="0 0 1024 1024" version="1.1" xmlns="http://www.w3.org/2000/svg" p-id="4309" xmlns:xlink="http://www.w3.org/1999/xlink" width="200" height="200"><path d="M381.792 375.392V314.368a269.76 269.76 0 0 1 17.92-0.768h0.16c167.328-5.28 277.024 143.968 277.024 143.968s-118.368 164.32-245.344 164.32a156.576 156.576 0 0 1-49.408-7.904v-185.44c65.184 7.904 78.368 36.576 117.216 101.76l87.04-73.12s-63.648-83.296-170.656-83.296a262.08 262.08 0 0 0-35.136 1.6l1.184-0.096z m0-202.048v91.232l18.08-1.152c232.544-7.904 384.416 190.72 384.416 190.72s-174.08 211.808-355.424 211.808c-15.776 0-31.264-1.504-46.72-4.128v56.544c12.8 1.504 26.016 2.656 38.816 2.656 168.832 0 290.976-86.304 409.312-188.064 19.584 15.84 99.84 53.888 116.48 70.496-112.352 94.208-374.272 169.984-522.784 169.984-14.304 0-27.872-0.768-41.44-2.272v79.52h641.44V173.408z m0 440.576v48.256C225.76 634.272 182.4 471.872 182.4 471.872s75.008-82.944 199.392-96.512v52.768h-0.352c-65.184-7.936-116.48 53.12-116.48 53.12s29.024 102.912 116.864 132.704z m-276.992-148.864s92.32-136.416 277.344-150.752V264.544C177.216 281.152 0.032 454.496 0.032 454.496s100.256 290.208 381.792 316.576v-52.768c-206.496-25.6-276.992-253.28-276.992-253.28z" fill="#76B900" p-id="4310"></path></svg>
|
||||
|
After Width: | Height: | Size: 1.4 KiB |
@ -20,6 +20,7 @@ export const IconMap = {
|
||||
OpenRouter: 'open-router',
|
||||
LocalAI: 'local-ai',
|
||||
StepFun: 'stepfun',
|
||||
NVIDIA:'nvidia'
|
||||
};
|
||||
|
||||
export const BedrockRegionList = [
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user