fastLLaMa
fastLLaMa is an experimental high-performance framework designed to tackle the challenges associated with deploying large language models (LLMs) in production environments.
It offers a user-friendly Python interface to a C++ library, llama.cpp, enabling developers to create custom workflows, implement adaptable logging, and seamlessly switch contexts between sessions. This framework is geared towards enhancing the efficiency of operating LLMs at scale, with ongoing development focused on introducing features such as optimized cold boot times, Int4 support for NVIDIA GPUs, model artifact management, and multiple programming language support.
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Features
Supported Models
Requirements
-
CMake
-
For Linux:
sudo apt-get -y install cmake
-
For OS X:
brew install cmake
-
For Windows
Download cmake-*.exe installer from Download page and run it.
-
GCC 11 or greater
-
Minimum C++ 17
-
Python 3.x
Installation
To install fastLLaMa through pip use
pip install git+https://github.com/PotatoSpudowski/fastLLaMa.git@main
Usage
Importing the package
To import fastLLaMa just run
from fastllama import Model
Initializing the Model
MODEL_PATH = "./models/7B/ggml-model-q4_0.bin"
model = Model(
path=MODEL_PATH, #path to model
num_threads=8, #number of threads to use
n_ctx=512, #context size of model
last_n_size=64, #size of last n tokens (used for repetition penalty) (Optional)
seed=0, #seed for random number generator (Optional)
n_batch=128, #batch size (Optional)
use_mmap=False, #use mmap to load model (Optional)
)
Ingesting Prompts
prompt = """Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.
User: Hello, Bob.
Bob: Hello. How may I help you today?
User: Please tell me the largest city in Europe.
Bob: Sure. The largest city in Europe is Moscow, the capital of Russia.
User: """
res = model.ingest(prompt, is_system_prompt=True) #ingest model with prompt
Generating Output
def stream_token(x: str) -> None:
"""
This function is called by the library to stream tokens
"""
print(x, end='', flush=True)
res = model.generate(
num_tokens=100,
top_p=0.95, #top p sampling (Optional)
temp=0.8, #temperature (Optional)
repeat_penalty=1.0, #repetition penalty (Optional)
streaming_fn=stream_token, #streaming function
stop_words=["User:", "\n"] #stop generation when this word is encountered (Optional)
)
Loading model using Multithreads
model = Model(
path=MODEL_PATH, #path to model
num_threads=8, #number of threads to use
n_ctx=512, #context size of model
last_n_size=64, #size of last n tokens (used for repetition penalty) (Optional)
seed=0, #seed for random number generator (Optional)
n_batch=128, #batch size (Optional)
load_parallel=True
)
Saving Model State
To cache the session, you can use the save_state method.
res = model.save_state("./models/fast_llama.bin")
Loading Model State
To load the session, use the load_state method.
res = model.load_state("./models/fast_llama.bin")
Resetting the Model State
To reset the session use the reset method.
model.reset()
Attaching LoRA Adapters to Base model during runtime
To attach LoRA Adapter during runtime use the attach_lora method.
LORA_ADAPTER_PATH = "./models/ALPACA-7B-ADAPTER/ggml-adapter-model.bin"
model.attach_lora(LORA_ADAPTER_PATH)
Note: It is a good idea to reset the state of the model after attaching a LoRA Adapter.
Detaching LoRA Adapters to Base model during runtime
To detach LoRA Adapter during runtime use the detach_lora method.
model.detach_lora()
Calculating perplexity
To caculate the perplexity, use the perplexity method.
with open("test.txt", "r") as f:
data = f.read(8000)
total_perplexity = model.perplexity(data)
print(f"Total Perplexity: {total_perplexity:.4f}")
Getting the embeddings of the model
To get the embeddings of the model, use the get_embeddings method.
embeddings = model.get_embeddings()
Getting the logits of the model
To get the logits of the model, use the get_logits method.
logits = model.get_logits()
Using the logger
from fastLLaMa import Logger
class MyLogger(Logger):
def __init__(self):
super().__init__()
self.file = open("logs.log", "w")
def log_info(self, func_name: str, message: str) -> None:
#Modify this to do whatever you want when you see info logs
print(f"[Info]: Func('{func_name}') {message}", flush=True, end='', file=self.file)
pass
def log_err(self, func_name: str, message: str) -> None:
#Modify this to do whatever you want when you see error logs
print(f"[Error]: Func('{func_name}') {message}", flush=True, end='', file=self.file)
def log_warn(self, func_name: str, message: str) -> None:
#Modify this to do whatever you want when you see warning logs
print(f"[Warn]: Func('{func_name}') {message}", flush=True, end='', file=self.file)
For more clarity, check the examples/python/ folder.
Running LLaMA
# obtain the original LLaMA model weights and place them in ./models
ls ./models
65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model
# convert the 7B model to ggml FP16 format
# python [PythonFile] [ModelPath] [Floattype] [Vocab Only] [SplitType]
python3 scripts/convert-pth-to-ggml.py models/7B/ 1 0
# quantize the model to 4-bits
./build/src/quantize models/7B/ggml-model-f16.bin models/7B/ggml-model-q4_0.bin 2
# run the inference
#Run the scripts from the root dir of the project for now!
python ./examples/python/example.py
Running Alpaca-LoRA
# Before running this command
# You need to provide the HF model paths here
python ./scripts/export-from-huggingface.py
# Alternatively you can just download the ggml models from huggingface directly and run them!
python3 ./scripts/convert-pth-to-ggml.py models/ALPACA-LORA-7B 1 0
./build/src/quantize models/ALPACA-LORA-7B/ggml-model-f16.bin models/ALPACA-LORA-7B/alpaca-lora-q4_0.bin 2
python ./examples/python/example-alpaca.py
Using LoRA adapters during runtime
# Download lora adapters and paste them inside models folder
# https://huggingface.co/tloen/alpaca-lora-7b
python scripts/convert-lora-to-ggml.py models/ALPACA-7B-ADAPTER/ -t fp32
# Change -t to fp16 to use fp16 weights
# Inorder to use LoRA adapters without caching, pass the --no-cache flag
# - Only supported for fp32 adapter weights
python examples/python/example-lora-adapter.py
# Make sure to set paths correctly for the base model and adapter inside the example
# Commands:
# load_lora: Attaches the adapter to the base model
# unload_lora: Deattaches the adapter (Deattach for fp16 is yet to be added!)
# reset: Resets the model state
Running the webUI
To run the WebSocket Server and the WebUI, Follow the instructions on the respective branches.
Memory/Disk Requirements
As the models are currently fully loaded into memory, you will need adequate disk space to save them
and sufficient RAM to load them. At the moment, memory and disk requirements are the same.
| model size |
original size |
quantized size (4-bit) |
| 7B |
13 GB |
3.9 GB |
| 13B |
24 GB |
7.8 GB |
| 30B |
60 GB |
19.5 GB |
| 65B |
120 GB |
38.5 GB |
Info: Run time may require extra memory during inference!
(Depends on hyperparmeters used during model initialization)
Contributing
- Contributors can open PRs
- Collaborators can push to branches to the repo and merge PRs into the main branch
- Collaborators will be invited based on contributions
- Any help with managing issues and PRs is very appreciated!
- Make sure to read about our vision
Notes
- Tested on
- Hardware: Apple silicon, Intel, Arm (Pending)
- OS: MacOs, Linux, Windows (Pending), Android (Pending)
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