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docs: writeup for running quivr fully locally (#1096)
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docs/docs/get_started/run_fully_local.md
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docs/docs/get_started/run_fully_local.md
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---
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sidebar_position: 2
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title: Using Quivr fully locally
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---
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# Using Quivr fully locally
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## Headers
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The following is a guide to set up everything for using Quivr locally:
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##### Table of Contents
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* [Database](#database)
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* [Embeddings](#embeddings)
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* [LLM for inference](#llm)
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It is a first, working setup, but a lot of work has to be done to e.g. find the appropriate settings for the model.
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Importantly, this will currently only work on tag v0.0.46.
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The guide was put together in collaboration with members of the Quivr Discord, **Using Quivr fully locally** thread. That is a good place to discuss it.
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This worked for me, but I sometimes got strange results (the output contains repeating answers/questions). Maybe because `stopping_criteria=stopping_criteria` must be uncommented in `transformers.pipeline`. Will update this page as I continue learning.
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<a name="database"/>
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## Local Supabase
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Instead of relying on a remote Supabase instance, we have to set it up locally. Follow the instructions on https://supabase.com/docs/guides/self-hosting/docker.
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Troubleshooting:
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* If the Quivr backend container cannot reach Supabase on port 8000, change the Quivr backend container to use the host network.
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* If email service does not work, add a user using the supabase web ui, and check "Auto Confirm User?".
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* http://localhost:8000/project/default/auth/users
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<a name="embeddings"/>
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## Local embeddings
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First, let's get local embeddings to work with GPT4All. Instead of relying on OpenAI for generating embeddings of both the prompt and the documents we upload, we will use a local LLM for this.
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Remove any existing data from the postgres database:
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* `supabase/docker $ docker compose down -v`
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* `supabase/docker $ rm -rf volumes/db/data/`
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* `supabase/docker $ docker compose up -d`
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Change the vector dimensions in the necessary Quivr SQL files:
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* Replace all occurrences of 1536 by 768, in Quivr's `scripts\tables.sql`
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* Run tables.sql in the Supabase web ui SQL editor: http://localhost:8000
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Change the Quivr code to use local LLM (GPT4All) and local embeddings:
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* add code to `backend\core\llm\private_gpt4all.py`
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```python
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from langchain.embeddings import HuggingFaceEmbeddings
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...
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def embeddings(self) -> HuggingFaceEmbeddings:
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emb = HuggingFaceEmbeddings(
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model_name="sentence-transformers/all-mpnet-base-v2",
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model_kwargs={'device': 'cuda'},
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encode_kwargs={'normalize_embeddings': False}
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)
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return emb
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```
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Note that there may be better models out there for generating the embeddings: https://huggingface.co/spaces/mteb/leaderboard
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Update Quivr `backend/core/.env`'s Private LLM Variables:
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```
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#Private LLM Variables
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PRIVATE=True
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MODEL_PATH=./local_models/ggml-gpt4all-j-v1.3-groovy.bin
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```
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Download GPT4All model:
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* `$ cd backend/core/local_models/`
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* `wget https://gpt4all.io/models/ggml-gpt4all-j-v1.3-groovy.bin`
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Ensure the Quivr backend docker container has CUDA and the GPT4All package:
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```
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FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-devel
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#FROM python:3.11-bullseye
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ARG DEBIAN_FRONTEND=noninteractive
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ENV DEBIAN_FRONTEND=noninteractive
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RUN pip install gpt4all
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```
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Modify the docker-compose yml file (for backend container). The following example is for using 2 GPUs:
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```
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...
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network_mode: host
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 2
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capabilities: [gpu]
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```
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Install nvidia container toolkit on the host, https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html:
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```
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$ wget https://nvidia.github.io/nvidia-docker/gpgkey --no-check-certificate
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$ sudo apt-key add gpgkey
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$ distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
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$ curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
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$ sudo apt-get update
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$ sudo apt-get install -y nvidia-container-toolkit
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$ nvidia-ctk --version
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$ sudo systemctl restart docker
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```
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At this moment, if we try to upload a pdf, we get an error:
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```
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backend-core | 1989-01-01 21:51:41,211 [ERROR] utils.vectors: Error creating vector for document {'code': '22000', 'details': None, 'hint': None, 'message': 'expected 768 dimensions, not 1536'}
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```
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This can be remedied by using local embeddings for document embeddings. In backend/core/utils/vectors.py, replace:
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```python
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# def create_vector(self, doc, user_openai_api_key=None):
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# logger.info("Creating vector for document")
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# logger.info(f"Document: {doc}")
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# if user_openai_api_key:
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# self.commons["documents_vector_store"]._embedding = OpenAIEmbeddings(
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# openai_api_key=user_openai_api_key
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# ) # pyright: ignore reportPrivateUsage=none
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# try:
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# sids = self.commons["documents_vector_store"].add_documents([doc])
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# if sids and len(sids) > 0:
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# return sids
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# except Exception as e:
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# logger.error(f"Error creating vector for document {e}")
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def create_vector(self, doc, user_openai_api_key=None):
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logger.info("Creating vector for document")
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logger.info(f"Document: {doc}")
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self.commons["documents_vector_store"]._embedding = HuggingFaceEmbeddings(
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model_name="sentence-transformers/all-mpnet-base-v2",
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model_kwargs={'device': 'cuda'},
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encode_kwargs={'normalize_embeddings': False}
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) # pyright: ignore reportPrivateUsage=none
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logger.info('||| creating emedding')
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try:
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sids = self.commons["documents_vector_store"].add_documents([doc])
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if sids and len(sids) > 0:
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return sids
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except Exception as e:
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logger.error(f"Error creating vector for document {e}")
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```
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<a name="llm"/>
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## Local LLM
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The final step is to use a local model from HuggingFace for inference. (The HF token is optional, only required for certain models on HF.)
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Update the Quivr backend dockerfile:
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```
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ENV HUGGINGFACEHUB_API_TOKEN=hf_XXX
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RUN pip install accelerate
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```
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Update the `private_gpt4all.py` file as follows:
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```python
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import langchain
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langchain.debug = True
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langchain.verbose = True
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import os
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import transformers
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from langchain.llms import HuggingFacePipeline
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from langchain.embeddings import HuggingFaceEmbeddings
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...
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model_id = "stabilityai/StableBeluga-13B"
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...
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def _create_llm(
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self,
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model,
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streaming=False,
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callbacks=None,
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) -> BaseLLM:
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"""
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Override the _create_llm method to enforce the use of a private model.
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:param model: Language model name to be used.
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:param streaming: Whether to enable streaming of the model
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:param callbacks: Callbacks to be used for streaming
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:return: Language model instance
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"""
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model_path = self.model_path
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logger.info("Using private model: %s", model)
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logger.info("Streaming is set to %s", streaming)
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logger.info("--- model %s",model)
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logger.info("--- model path %s",model_path)
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model_id = "stabilityai/StableBeluga-13B"
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llm = transformers.AutoModelForCausalLM.from_pretrained(
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model_id,
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use_cache=True,
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load_in_4bit=True,
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device_map='auto',
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#use_auth_token=hf_auth
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)
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logger.info('<<< transformers.AutoModelForCausalLM.from_pretrained')
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llm.eval()
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logger.info('<<< eval')
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tokenizer = transformers.AutoTokenizer.from_pretrained(
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model_id,
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use_auth_token=hf_auth
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)
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logger.info('<<< transformers.AutoTokenizer.from_pretrained')
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generate_text = transformers.pipeline(
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model=llm, tokenizer=tokenizer,
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return_full_text=True, # langchain expects the full text
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task='text-generation',
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# we pass model parameters here too
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#stopping_criteria=stopping_criteria, # without this model rambles during chat
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temperature=0.5, # 'randomness' of outputs, 0.0 is the min and 1.0 the max
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max_new_tokens=512, # mex number of tokens to generate in the output
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repetition_penalty=1.1 # without this output begins repeating
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)
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logger.info('<<< generate_text = transformers.pipeline(')
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result = HuggingFacePipeline(pipeline=generate_text)
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logger.info('<<< generate_text = transformers.pipeline(')
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logger.info("<<< created llm HuggingFace")
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return result
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```
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