mirror of
https://github.com/hwchase17/langchain
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Qwen
model example (#9516)
added an example for `Qwen-7B` model on `HugginfFaceHub` 🤗
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 1,
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"id": "d597a792-354c-4ca5-b483-5965eec5d63d",
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"metadata": {},
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"outputs": [
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"id": "b8c5b88c-e4b8-4d0d-9a35-6e8f106452c2",
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"metadata": {},
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"outputs": [],
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": null,
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"id": "3fe7d1d1-241d-426a-acff-e208f1088871",
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"metadata": {},
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"outputs": [],
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 4,
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"id": "6620f39b-3d32-4840-8931-ff7d2c3e47e8",
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"metadata": {},
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"outputs": [],
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 5,
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"id": "44adc1a0-9c0a-4f1e-af5a-fe04222e78d7",
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"metadata": {},
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"outputs": [],
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@ -358,10 +358,48 @@
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"print(llm_chain.run(question))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4f2e5132-1713-42d7-919a-8c313744ce95",
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"metadata": {},
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"source": [
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"### `Qwen`, by `Alibaba Cloud`\n",
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"\n",
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">`Tongyi Qianwen-7B` (`Qwen-7B`) is a model with a scale of 7 billion parameters in the `Tongyi Qianwen` large model series developed by `Alibaba Cloud`. `Qwen-7B` is a large language model based on Transformer, which is trained on ultra-large-scale pre-training data.\n",
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"\n",
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"See [more information on HuggingFace](https://huggingface.co/Qwen/Qwen-7B) of on [GitHub](https://github.com/QwenLM/Qwen-7B).\n",
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"\n",
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"See here a [big example for LangChain integration and Qwen](https://github.com/QwenLM/Qwen-7B/blob/main/examples/langchain_tooluse.ipynb)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "f598b1ca-77c7-40f1-a83f-c21ea9910c88",
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"metadata": {},
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"outputs": [],
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"source": [
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"repo_id = \"Qwen/Qwen-7B\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a80bc30a-4040-417f-8094-d2c81c423b76",
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"id": "2c97f4e2-d401-44fb-9da7-b60b2e2cc663",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = HuggingFaceHub(\n",
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" repo_id=repo_id, model_kwargs={\"max_length\": 128, \"temperature\": 0.5}\n",
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")\n",
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"llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
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"print(llm_chain.run(question))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1dd67c1e-1efc-4def-bde4-2e5265725303",
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"metadata": {},
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"outputs": [],
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"source": []
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