LLMs

Large-language models are AI models that can understand and generate text, primarily using transformer architectures. This page is about running them on a HPC cluster. This requires programming experience and knowledge of using the cluster (Tutorials), but allows maximum computational power for the least cost. Aalto RSE maintains these models and can provide help with using these, even to users who aren’t computational experts.

Because the size of model weights are typically very large and the interest in the models are high, so we provide our users with pre-downloaded model weights in various format, along with instructions on how to load these weights for inference purposes, retraining, and fine-tuning tasks.

HuggingFace Models

The simplest way to use an open-source LLM(Large Language Model) is through the tools and pre-trained models hub from huggingface. Huggingface is a popular platform for NLP(Natural Language Processing) tasks. It provides a user-friendly interface through the transformers library to load and run various pre-trained models. Most open-source models from Huggingface are widely supported and integrated with the transformers library. We are keeping our eyes on the latest models and have downloaded some of them for you. The full list of all the available models are located at /scratch/shareddata/dldata/huggingface-hub-cache/models.txt. Please contact us if you need any other models. The following table lists only a few example from the hosted models:

Model type	Huggingface model identifier
Text Generation	meta-llama/Meta-Llama-3-8B
Text Generation	meta-llama/Meta-Llama-3-8B-Instruct
Text Generation	mistralai/Mixtral-8x22B-v0.1
Text Generation	mistralai/Mixtral-8x22B-Instruct-v0.1
Text Generation	tiiuae/falcon-40b
Text Generation	tiiuae/falcon-40b-instruct
Text Generation	google/gemma-2b-it
Text Generation	google/gemma-7b
Text Generation	google/gemma-7b-it
Text Generation	google/gemma-7b
Text Generation	LumiOpen/Poro-34B
Text Generation	meta-llama/Llama-2-7b-hf
Text Generation	meta-llama/Llama-2-13b-hf
Text Generation	meta-llama/Llama-2-70b-hf
Text Generation	codellama/CodeLlama-7b-hf
Text Generation	codellama/CodeLlama-13b-hf
Text Generation	codellama/CodeLlama-34b-hf
Translation	Helsinki-NLP/opus-mt-en-fi
Translation	Helsinki-NLP/opus-mt-fi-en
Translation	t5-base
Fill Mask	bert-base-uncased
Fill Mask	bert-base-cased
Fill Mask	distilbert-base-uncased
Text to Speech	microsoft/speecht5_hifigan
Text to Speech	facebook/hf-seamless-m4t-large
Automatic Speech Recognition	openai/whisper-large-v3
Token Classification	dslim/bert-base-NER-uncased

To access Huggingface models:

Load the module to setup the environment variable HF_HOME:

module load model-huggingface/all
# this will set HF_HOME to /scratch/shareddata/dldata/huggingface-hub-cache

In jupyter notebook, one can set up HF_HOME directly:

import os
os.environ['TRANSFORMERS_OFFLINE'] = '1'
os.environ['HF_HOME']='/scratch/shareddata/dldata/huggingface-hub-cache'

Here is a Python script using huggingface model.

## Force transformer to load model(s) from local hub instead of download and load model(s) from remote hub. NOTE: this must be run before importing transformers.
import os
os.environ['TRANSFORMERS_OFFLINE'] = '1'

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")

prompt = "How many stars in the space?"

model_inputs = tokenizer([prompt], return_tensors="pt")
input_length = model_inputs.input_ids.shape[1]

generated_ids = model.generate(**model_inputs, max_new_tokens=20)
print(tokenizer.batch_decode(generated_ids[:, input_length:], skip_special_tokens=True)[0])

Raw model weights

We also downloaded the following raw llama model weights (PyTorch model checkpoints), and they are managed by the following modules.

Model type	Model version	Module command to load	Description
Llama 2	Raw Data	`module load model-llama2/raw-data`	Raw weights of Llama 2.
Llama 2	7b	`module load model-llama2/7b`	Raw weights of 7B parameter version of Llama 2.
Llama 2	7b-chat	`module load model-llama2/7b-chat`	Raw weights of 7B parameter chat optimized version of Llama 2.
Llama 2	13b	`module load model-llama2/13b`	Raw weights of 13B parameter version of Llama 2.
Llama 2	13b-chat	`module load model-llama2/13b-chat`	Raw weights of 13B parameter chat optimized version of Llama 2.
Llama 2	70b	`module load model-llama2/70b`	Raw weights of 70B parameter version of Llama 2.
Llama 2	70b-chat	`module load model-llama2/70b-chat`	Raw weights of 70B parameter chat optimized version of Llama 2.
CodeLlama	Raw Data	`module load model-codellama/raw-data`	Raw weights of CodeLlama.
CodeLlama	7b	`module load model-codellama/7b`	Raw weights of 7B parameter version of CodeLlama.
CodeLlama	7b-Python	`module load model-codellama/7b-python`	Raw weights of 7B parameter version CodeLlama, specifically designed for Python.
CodeLlama	7b-Instruct	`module load model-codellama/7b-instruct`	Raw weights of 7B parameter version CodeLlama, designed for instruction following.
CodeLlama	13b	`module load model-codellama/13b`	Raw weights of 13B parameter version of CodeLlama.
CodeLlama	13b-Python	`module load model-codellama/13b-python`	Raw weights of 13B parameter version CodeLlama, specifically designed for Python.
CodeLlama	13b-Instruct	`module load model-codellama/13b-instruct`	Raw weights of 13B parameter version CodeLlama, designed for instruction following.
CodeLlama	34b	`module load model-codellama/34b`	Raw weights of 34B parameter version of CodeLlama.
CodeLlama	34b-Python	`module load model-codellama/34b-python`	Raw weights of 34B parameter version CodeLlama, specifically designed for Python.
CodeLlama	34b-Instruct	`module load model-codellama/34b-instruct`	Raw weights of 34B parameter version CodeLlama, designed for instruction following.

Each module will set the following environment variables:

MODEL_ROOT - Folder where model weights are stored, i.e., PyTorch model checkpoint directory.
TOKENIZER_PATH - File path to the tokenizer.model.

Here is an example slurm script, using the raw weights for batch inference. For detailed environment setting up, example prompts and Python code, please check out this repo.

#!/bin/bash
#SBATCH --time=00:25:00
#SBATCH --cpus_per_task=4
#SBATCH --mem=20GB
#SBATCH --gpus=1
#SBATCH --output=llama2inference-gpu.%J.out
#SBATCH --error=llama2inference-gpu.%J.err

# get access to the model weights
module load model-llama2/7b
echo $MODEL_ROOT
# Expect output: /scratch/shareddata/dldata/llama-2/llama-2-7b
echo $TOKENIZER_PATH
# Expect output: /scratch/shareddata/dldata/llama-2/tokenizer.model

# activate your conda environment
module load mamba
source activate llama2env

# run batch inference
torchrun --nproc_per_node 1 batch_inference.py \
  --prompts prompts.json \
  --ckpt_dir $MODEL_ROOT \
  --tokenizer_path $TOKENIZER_PATH \
  --max_seq_len 512 --max_batch_size 16

llama.cpp and GGUF model weights

llama.cpp is another popular framework for running inference on LLM models with CPUs or GPUs. It provides C++ implementations of many large language models. llama.cpp uses a format called GGUF as its storage format. We have GGUF conversions of all Llama 2 and CodeLlama models with multiple quantization levels. Please contact us if you need any other GGUF models. NOTE: Before loading the following modules, one must first load a module for the raw model weights. For example, run module load model-codellama/34b first, and then run module load codellama.cpp/q8_0-2023-12-04 to get the 8-bit integer version of CodeLlama weights in a .gguf file.

Model type	Model version	Module command to load	Description
Llama 2	f16-2023-08-28	`module load model-llama.cpp/f16-2023-12-04` (after loading a Llama 2 model for some raw weights)	Half precision version of Llama 2 weights done with llama.cpp on 4th of Dec 2023.
Llama 2	q4_0-2023-08-28	`module load model-llama.cpp/q4_0-2023-12-04` (after loading a Llama 2 model for some raw weights)	4-bit integer version of Llama 2 weights done with llama.cpp on 4th of Dec 2023.
Llama 2	q4_1-2023-08-28	`module load model-llama.cpp/q4_1-2023-12-04` (after loading a Llama2 model for some raw weights)	4-bit integer version of Llama 2 weights done with llama.cpp on 4th of Dec 2023.
Llama 2	q8_0-2023-08-28	`module load model-llama.cpp/q8_0-2023-12-04` (after loading a Llama 2 model for some raw weights)	8-bit integer version of Llama 2 weights done with llama.cpp on 4th of Dec 2023.
CodeLlama	f16-2023-08-28	`module load codellama.cpp/f16-2023-12-04` (after loading a CodeLlama model for some raw weights)	Half precision version of CodeLlama weights done with llama.cpp on 4th of Dec 2023.
CodeLlama	q4_0-2023-08-28	`module load codellama.cpp/q4_0-2023-12-04` (after loading a CodeLlama model for some raw weights)	4-bit integer version of CodeLlama weights done with llama.cpp on 4th of Dec 2023.
CodeLlama	q8_0-2023-08-28	`module load codellama.cpp/q8_0-2023-12-04` (after loading a CodeLlama model for some raw weights)	8-bit integer version of CodeLlama weights done with llama.cpp on 4th of Dec 2023.

Each module will set the following environment variables:

MODEL_ROOT - Folder where model weights are stored.
MODEL_WEIGHTS - Path to the model weights in GGUF file format.

This Python code snippet is part of a ‘Chat with Your PDF Documents’ example, utilizing LangChain and leveraging model weights stored in a .gguf file. For detailed environment setting up and Python code, please check out this repo.

import os
from langchain.llms import LlamaCpp

model_path = os.environ.get('MODEL_WEIGHTS')
llm = LlamaCpp(model_path=model_path, verbose=False)

More examples

Starting a local API

With the pre-downloaded model weights, you are also able create an API endpoint locally. For detailed examples, you can checkout this repo.