mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
Add HuggingFace Examples (#3187)
Add a Pipeline example and add other models in th ehub notebook To close issue [#3077](https://github.com/hwchase17/langchain/issues/3099)
This commit is contained in:
Zander Chase
GitHub
parent
6adf2d1c39
commit
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@ -31,7 +31,7 @@
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"!pip install huggingface_hub"
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"!pip install huggingface_hub > /dev/null"
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]
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]
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},
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},
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{
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@ -55,41 +55,195 @@
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"import os\n",
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"os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = HUGGINGFACEHUB_API_TOKEN"
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"os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = HUGGINGFACEHUB_API_TOKEN"
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]
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]
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},
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},
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{
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"cell_type": "markdown",
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"id": "84dd44c1-c428-41f3-a911-520281386c94",
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"metadata": {},
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"source": [
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"**Select a Model**"
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]
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": null,
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"id": "39c7eeac-01c4-486b-9480-e828a9e73e78",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain import HuggingFaceHub\n",
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"\n",
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"repo_id = \"google/flan-t5-xl\" # See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options\n",
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"\n",
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"llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\":0, \"max_length\":64})"
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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": "3acf0069",
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"id": "3acf0069",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [],
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The FIFA World Cup is a football tournament that is played every 4 years. The year 1994 was the 44th FIFA World Cup. The final answer: Brazil.\n"
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]
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}
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],
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"source": [
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"source": [
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"from langchain import PromptTemplate, HuggingFaceHub, LLMChain\n",
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"from langchain import PromptTemplate, LLMChain\n",
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"\n",
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"\n",
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"template = \"\"\"Question: {question}\n",
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"template = \"\"\"Question: {question}\n",
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"\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
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"llm_chain = LLMChain(prompt=prompt, llm=HuggingFaceHub(repo_id=\"google/flan-t5-xl\", model_kwargs={\"temperature\":0, \"max_length\":64}))\n",
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"llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
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"\n",
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"\n",
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"question = \"Who won the FIFA World Cup in the year 1994? \"\n",
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"question = \"Who won the FIFA World Cup in the year 1994? \"\n",
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"\n",
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"\n",
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"print(llm_chain.run(question))"
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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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},
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{
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"cell_type": "markdown",
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"id": "ddaa06cf-95ec-48ce-b0ab-d892a7909693",
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"metadata": {},
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"source": [
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"## Examples\n",
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"\n",
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"Below are some examples of models you can access through the Hugging Face Hub integration."
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]
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},
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{
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"cell_type": "markdown",
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"id": "4fa9337e-ccb5-4c52-9b7c-1653148bc256",
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"metadata": {},
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"source": [
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"### StableLM, by Stability AI\n",
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"\n",
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"See [Stability AI's](https://huggingface.co/stabilityai) organization page for a list of available models."
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]
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": null,
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"execution_count": null,
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"id": "843a3837",
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"id": "36a1ce01-bd46-451f-8ee6-61c8f4bd665a",
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"metadata": {},
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"outputs": [],
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"source": [
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"repo_id = \"stabilityai/stablelm-tuned-alpha-3b\"\n",
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"# Others include stabilityai/stablelm-base-alpha-3b\n",
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"# as well as 7B parameter versions"
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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": "b5654cea-60b0-4f40-ab34-06ba1eca810d",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\":0, \"max_length\":64})"
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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": "2f19d0dc-c987-433f-a8d6-b1214e8ee067",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Reuse the prompt and question from above.\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": "markdown",
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"id": "1a5c97af-89bc-4e59-95c1-223742a9160b",
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"metadata": {},
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"source": [
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"### Dolly, by DataBricks\n",
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"\n",
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"See [DataBricks](https://huggingface.co/databricks) organization page for a list of available models."
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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": "521fcd2b-8e38-4920-b407-5c7d330411c9",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain import HuggingFaceHub\n",
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"\n",
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"repo_id = \"databricks/dolly-v2-3b\"\n",
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"\n",
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"llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\":0, \"max_length\":64})"
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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": "9907ec3a-fe0c-4543-81c4-d42f9453f16c",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Reuse the prompt and question from above.\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": "markdown",
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"id": "03f6ae52-b5f9-4de6-832c-551cb3fa11ae",
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"metadata": {},
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"source": [
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"### Camel, by Writer\n",
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"\n",
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"See [Writer's](https://huggingface.co/Writer) organization page for a list of available models."
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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": "257a091d-750b-4910-ac08-fe1c7b3fd98b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain import HuggingFaceHub\n",
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"\n",
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"repo_id = \"Writer/camel-5b-hf\" # See https://huggingface.co/Writer for other options\n",
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"llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\":0, \"max_length\":64})"
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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": "b06f6838-a11a-4d6a-88e3-91fa1747a2b3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Reuse the prompt and question from above.\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": "markdown",
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"id": "2bf838eb-1083-402f-b099-b07c452418c8",
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"metadata": {},
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"source": [
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"**And many more!**"
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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": "18c78880-65d7-41d0-9722-18090efb60e9",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": []
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"source": []
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"name": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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"version": "3.11.2"
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}
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}
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},
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},
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"nbformat": 4,
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"nbformat": 4,
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{
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"cells": [
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"cell_type": "markdown",
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"id": "959300d4",
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"metadata": {},
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"source": [
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"# Hugging Face Local Pipelines\n",
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"\n",
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"Hugging Face models can be run locally through the `HuggingFacePipeline` class.\n",
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"\n",
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"The [Hugging Face Model Hub](https://huggingface.co/models) hosts over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.\n",
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"\n",
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"These can be called from LangChain either through this local pipeline wrapper or by calling their hosted inference endpoints through the HuggingFaceHub class. For more information on the hosted pipelines, see the [HugigngFaceHub](huggingface_hub.ipynb) notebook."
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]
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},
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{
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"cell_type": "markdown",
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"id": "4c1b8450-5eaf-4d34-8341-2d785448a1ff",
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"metadata": {
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"tags": []
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},
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"source": [
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"To use, you should have the ``transformers`` python [package installed](https://pypi.org/project/transformers/)."
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]
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},
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"cell_type": "code",
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"execution_count": 1,
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"id": "d772b637-de00-4663-bd77-9bc96d798db2",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"!pip install transformers > /dev/null"
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]
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},
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{
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"cell_type": "markdown",
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"id": "91ad075f-71d5-4bc8-ab91-cc0ad5ef16bb",
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"metadata": {},
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"source": [
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"### Load the model"
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]
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},
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"cell_type": "code",
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"execution_count": 2,
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"id": "165ae236-962a-4763-8052-c4836d78a5d2",
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"metadata": {
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"tags": []
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING:root:Failed to default session, using empty session: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /sessions (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x1117f9790>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
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]
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}
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],
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"source": [
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"from langchain import HuggingFacePipeline\n",
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"\n",
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"llm = HuggingFacePipeline.from_model_id(model_id=\"bigscience/bloom-1b7\", task=\"text-generation\", model_kwargs={\"temperature\":0, \"max_length\":64})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "00104b27-0c15-4a97-b198-4512337ee211",
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"metadata": {},
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"source": [
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"### Integrate the model in an LLMChain"
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]
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"id": "3acf0069",
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"metadata": {},
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"outputs": [
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/wfh/code/lc/lckg/.venv/lib/python3.11/site-packages/transformers/generation/utils.py:1288: UserWarning: Using `max_length`'s default (64) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
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" warnings.warn(\n",
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"WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x144d06910>: Failed to establish a new connection: [Errno 61] Connection refused'))\n"
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]
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"name": "stdout",
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"text": [
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" First, we need to understand what is an electroencephalogram. An electroencephalogram is a recording of brain activity. It is a recording of brain activity that is made by placing electrodes on the scalp. The electrodes are placed\n"
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]
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}
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],
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"source": [
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"from langchain import PromptTemplate, LLMChain\n",
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"\n",
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
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"\n",
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"llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
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"\n",
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"question = \"What is electroencephalography?\"\n",
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"\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": "843a3837",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.11.2"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue
Block a user