Llama at Home

🦙@🏠: locally hosted generative agent using the llama LLM and langchain.

Work in progress

• Multi-step critic chains to be used for generative agent activities.
• Recursive planning of daily activities.
• Enacting planned daily activities.

Feature list

• Models:
  • llama with huggingface pipeline
  • alpaca with Peft models
  • llama.cpp with lora and/or quantization
• Tools:
  • Redis and Chroma vectorstores
  • text and pdf formats
  • wrappers for Wiki, Searx, docsearch tools.
• Chains:
  • Refined prompt and error handling for Langchain's MRKL agent executor
  • Improved chain sequence specification.
• Agent memory:
  • Storing long term memory using postgres vectorstores.
  • Implementing importance, relevance and recency calculations for long term memory retrieval.
• Agent reflection:
  • Reflecting on recalled memories.
  • Reflecting on reflections to revise self-identity statements.

Getting started

To obtain all the tools and development packages used in this repository:

git clone --recurse-submodules [email protected]:bowenwen/llama-at-home.git

Model and environment setup

Follow this section get your working environment ready to run langchain with llama at home. All of the code, instruction and setup assume you are using gpu (cuda). You may need to review source materials where I referenced to modify the code and instruction to run with cpu.

1. Install packages for text-generation-webui and langchain

Following instruction from text-generation-webui:

For additional package installation, see environments/langchain.sh

Not interested in getting the latest development version? Install versions from environments/langchain.yml

conda env create -f ./environments/langchain.yml

2. Obtain weights for the base model

Before you start, any downloaded or converted models need to be placed in the models/ folder. Both langchain and text-generation-webui will need to use the models, so you might want to consider using symbolic link to avoid needing to keep two copies, for example:

ln -s ~/projects/llama-at-home/models ~/projects/llama-at-home/tools/text-generation-webui/models
ln -s ~/projects/llama-at-home/loras ~/projects/llama-at-home/tools/text-generation-webui/loras
ln -s ~/projects/llama-at-home/tools/GPTQ-for-LLaMa ~/projects/llama-at-home/tools/text-generation-webui/repositories/GPTQ-for-LLaMa

Option 1: Download weights

Get the 4bit huggingface version 2 (HFv2) from here. Downloaded weights only work for a time, until transformer update its code and it will break it eventually. For more future-proof approach, try convert the weights yourself.

Option 2: Convert weights for Huggingface Pipeline

Request the original facebook weights. Then convert the weight to HFv2, detail. Note that since April 2023, convert_llama_weights_to_hf.py is now part of the transformer repo!

Step 1: convert weights to HuggingFace format

cd packages/transformers
# convert 7B model
python src/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir ../../models/facebook --model_size 7B --output_dir ../../models/hfv2
mv ../../models/hfv2 ../../models/llama-7b
# convert 13B model
python src/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir ../../models/facebook --model_size 13B --output_dir ../../models/hfv2
mv ../../models/hfv2 ../../models/llama-13b
# convert 30B model
python src/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir ../../models/facebook --model_size 30B --output_dir ../../models/hfv2
mv ../../models/hfv2 ../../models/llama-30b
# if your gpu is good enough to convert 65B model, please do share!
# done, going back to top directory
cd ../..

Step 2: conversion for quantized 4-bits weights

To conv ert Quantize to 4bit, read more here: https://github.com/oobabooga/text-generation-webui/wiki/LLaMA-model#4-bit-mode https://github.com/qwopqwop200/GPTQ-for-LLaMa.

For the most part, it is easier to download the 4-bit weights, but you can also follow Step 3 to convert it yourself.

Option 2: Convert weights for llama.cpp

Instead of running on pure python and using the transformer and pytorch ecosystem, there is an alternative way of running the llama model more efficiently on cpu using llama.cpp.

Step 1: convert base model weights

For more info, see instructions from llama.cpp. Please note that you can convert weights from either the original facebook weights or the huggingface v2 weights.

cd tools/llama.cpp/

# build the repo
make

# convert the models to ggml FP16 format
python convert.py ../../models/llama-7b/
python convert.py ../../models/llama-13b/
python convert.py ../../models/llama-30b/
python convert.py ../../models/llama-65b/

# quantize the model to 4-bits (using method 2 = q4_0)
./quantize ../../models/llama-7b/ggml-model-f16.bin ../../models/llama-7b/ggml-model-q4_0.bin 2
./quantize ../../models/llama-13b/ggml-model-f16.bin ../../models/llama-13b/ggml-model-q4_0.bin 2
./quantize ../../models/llama-30b/ggml-model-f16.bin ../../models/llama-30b/ggml-model-q4_0.bin 2
./quantize ../../models/llama-65b/ggml-model-f16.bin ../../models/llama-65b/ggml-model-q4_0.bin 2

# run the inference
./main -m ./models/7B/ggml-model-q4_0.bin -n 128

cd ../../

Step 2: convert lora weights

Lora support has been added to llama.cpp, see info here. To use it, you would need to have already trained your own loras or download one.

cd tools/llama.cpp/

# convert loras
python convert-lora-to-ggml.py ../../loras/alpaca-lora-7b
python convert-lora-to-ggml.py ../../loras/alpaca-gpt4-lora-13b-3ep
python convert-lora-to-ggml.py ../../loras/alpaca-lora-30b-chansung
python convert-lora-to-ggml.py ../../loras/alpaca-lora-65b-chansung

# run the inference with lora - Use the ggml-adapter-model.bin with --lora
./main -m models/llama-7b/ggml-model-f16.bin --lora loras/alpaca-lora-7b/ggml-adapter-model.bin --color -f ./prompts/alpaca.txt -ins -b 256 --top_k 10000 --temp 0.2 --repeat_penalty 1 -t 7

# run the inference with quantized model
./main -m models/llama-7b/ggml-model-q4_0.bin --lora loras/alpaca-lora-7b/ggml-adapter-model.bin --lora-base models/llama-7b/ggml-model-f16.bin --color -f ./prompts/alpaca.txt -ins -b 256 --top_k 10000 --temp 0.2 --repeat_penalty 1 -t 7

cd ../../

3. Fine tuning your model

Read more about fine tuning with alpaca-lora here

Train lora for llama-7b:

cd tools/alpaca-lora
python finetune.py \
    --base_model="../../models/llama-7b" \
    --data_path="alpaca_data_gpt4.json" \
    --num_epochs=10 \
    --cutoff_len=512 \
    --group_by_length \
    --output_dir="../../loras/alpaca-lora-7b" \
    --lora_target_modules="[q_proj,k_proj,v_proj,o_proj]" \
    --lora_r=16 \
    --micro_batch_size=8
cd ../..

Train lora for llama-13b:

cd tools/alpaca-lora
python finetune.py \
    --base_model="../../models/llama-13b" \
    --data_path="alpaca_data_gpt4.json" \
    --num_epochs=10 \
    --cutoff_len=512 \
    --group_by_length \
    --output_dir="../../loras/alpaca-lora-13b" \
    --lora_target_modules="[q_proj,k_proj,v_proj,o_proj]" \
    --lora_r=16 \
    --batch_size=128  \
    --micro_batch_size=4
cd ../..

If your training gets interpreted, you can continue the training by changing a few lines of code in finetune.py, see here

Examples of other fine tuning results, as with the converted weights, downloaded loras may not work with the latest versions of various packages:

• https://github.com/tloen/alpaca-lora#resources
• https://huggingface.co/tloen/alpaca-lora-7b
• https://huggingface.co/chansung/alpaca-lora-13b
• https://huggingface.co/chansung/alpaca-lora-30b
• https://huggingface.co/chansung/alpaca-lora-65b

4. Configuring model parameters for alpaca lora

Commonly, HuggingFace models will come with a config.json which you can use to add your own configurations, some common parameters are shown before. See HuggingFace Documentation for more info.

{
  "do_sample": 1,
  "temperature": 0.7,
  "top_p": 0.1,
  "typical_p": 1,
  "repetition_penalty": 1.18,
  "encoder_repetition_penalty": 1,
  "top_k": 40,
  "num_beams": 1,
  "penalty_alpha": 0,
  "min_length": 0,
  "length_penalty": 1,
  "no_repeat_ngram_size": 0,
  "early_stopping": 0,
}

• https://huggingface.co/blog/how-to-generate
• https://docs.cohere.ai/docs/controlling-generation-with-top-k-top-p

5. Finetuning 4-bit models

The process for finetuning 4-bit models is slight more complicated and require patching of some existing models. See johnsmith0031/alpaca_lora_4bit for more information. Note that with this set up, you may not be able to use 8 bit lora properly within reinstalling official version of peft and GPTQ-for-LLaMa.

Installation

cd tools/alpaca_lora_4bit
pip install -r requirements.txt

Other models

Trying out Cerebras-GPT

• Source: https://huggingface.co/cerebras

bash

cd ~/apps/vscodeserver/appdata/coder/projects/Cerebras-GPT
git lfs install
git clone https://huggingface.co/cerebras/Cerebras-GPT-111M
git clone https://huggingface.co/cerebras/Cerebras-GPT-256M
git clone https://huggingface.co/cerebras/Cerebras-GPT-590M
git clone https://huggingface.co/cerebras/Cerebras-GPT-1.3B
git clone https://huggingface.co/cerebras/Cerebras-GPT-2.7B
git clone https://huggingface.co/cerebras/Cerebras-GPT-6.7B
git clone https://huggingface.co/cerebras/Cerebras-GPT-13B

Using text-generation-webui

conda activate langchain

# use 8 bits
python server.py --listen --load-in-8bit --no-stream --model llama-7b
python server.py --listen --load-in-8bit --no-stream --model llama-13b

# Or, use 4 bits
python server.py --listen --model llama-7b-4bit-128g --wbits 4 --groupsize 128 --no-stream --chat
python server.py --listen --model llama-13b-4bit-128g --wbits 4 --groupsize 128 --chat
python server.py --listen --model llama-30b-4bit-128g --wbits 4 --groupsize 128 --chat

# Running with LoRA
python server.py --listen --model llama-7b --lora alpaca-lora-7b --load-in-8bit
python server.py --listen --model llama-13b --lora alpaca-lora-13b --load-in-8bit

# Starting API (same with regular but no chat), api at `http://{server}:7860/api/textgen`
python server.py --listen --listen-port 7860 --load-in-8bit --no-stream --model llama-7b
# --extensions api

You may need to update the model name if the downloaded version is slightly different.

Browse to: http://localhost:7860/?__theme=dark help?

Using LangChain

Set up documents

mkdir -p ./docs/arxiv/
cp ~/downloads/arxiv/* ./docs/arxiv/

Some reading materials on Langchain

• https://huggingface.co/blog/hf-bitsandbytes-integration
• https://huggingface.co/docs/transformers/v4.13.0/en/performance

Set up pgvector for memory store

The custom memory store module uses the langchain pgvector vectorstore with a set of custom metadata attributes to keep long term memories for langchain agents. In order to use memory store, you need to first set up a postgres server with pgvector store extension. The easiest way is to use docker to build and deploy your own postgres server for langchain:

cd tools/pgvector
docker build -t postgres:pgvector-15-latest .
docker run -d --expose 5432 postgres:pgvector-15-latest --volume ~/apps/db-data/:/var/lib/postgresql/data/

Finally, enable extension for pgvector. Run the following command, you must log into your postgres database that you are using for this application as the super user (usually postgres):

create extension vector;

conda env export --no-builds > ./environments/langchain.yml

## Resources
* make it work with UI: https://rentry.org/llama-tard-v2
* Fine tuning: https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/chatllama
* CPP implementation: https://github.com/ggerganov/llama.cpp
* FlexGen: https://github.com/FMInference/FlexGen
* https://github.com/intel/intel-extension-for-pytorch