metadata

language:
  - en
license: llama3
tags:
  - llama-3
  - llama
  - Mixtral
  - instruct
  - finetune
  - chatml
  - DPO
  - RLHF
  - gpt4
  - distillation
  - GGUF
base_model: meta-llama/Meta-Llama-3-70B-Instruct
widget:
  - example_title: OpenBioLLM-70B
    messages:
      - role: system
        content: >-
          You are an expert and experienced from the healthcare and biomedical
          domain with extensive medical knowledge and practical experience.
      - role: user
        content: How long does it take for newborn jaundice to go away?
    output:
      text: >-
        Newborn jaundice, also known as neonatal jaundice, is a common condition
        in newborns where the yellowing of the skin and eyes occurs due to an
        elevated level of bilirubin in the blood. Bilirubin is a yellow pigment
        that forms when red blood cells break down. In most cases, newborn
        jaundice resolves on its own without any specific treatment.

        The duration of newborn jaundice can vary depending on several factors
        such as the underlying cause, gestational age at birth, and individual
        variations in bilirubin metabolism. Here are some general guidelines:

        1. Physiological jaundice: This is the most common type of newborn
        jaundice and usually appears within 24-72 hours after birth. It tends to
        peak between the second and fifth day of life and gradually improves
        over the next week or two. By the time the baby is one week old, the
        jaundice should have mostly resolved. 2. Breast milk jaundice: This type
        of jaundice occurs in breastfed babies and may appear later than
        physiological jaundice, typically between the fifth and fourteenth day
        of life. It tends to persist for a longer duration but usually resolves
        within six weeks after birth. 3. Pathological jaundice: This type of
        jaundice is less common and occurs due to an underlying medical
        condition that affects bilirubin metabolism or liver function. The
        duration of pathological jaundice depends on the specific cause and may
        require treatment.

        It's important for parents to monitor their newborn's jaundice closely
        and seek medical advice if the jaundice progresses rapidly, becomes
        severe, or is accompanied by other symptoms such as poor feeding,
        lethargy, or excessive sleepiness. In these cases, further evaluation
        and management may be necessary. Remember that each baby is unique, and
        the timing of jaundice resolution can vary. If you have concerns about
        your newborn's jaundice, it's always best to consult with a healthcare
        professional for personalized advice and guidance.
model-index:
  - name: OpenBioLLM-70B
    results: []
quantized_by: andrijdavid

Llama3-OpenBioLLM-70B-GGUF

Original model: Llama3-OpenBioLLM-70B

Description

This repo contains GGUF format model files for Llama3-OpenBioLLM-70B.

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF:

llama.cpp. This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.
text-generation-webui, Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.
Ollama Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications
KoboldCpp, A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.
GPT4All, This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.
LM Studio An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.
LoLLMS Web UI. A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.
Faraday.dev, An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.
llama-cpp-python, A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.
candle, A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.
ctransformers, A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.
localGPT An open-source initiative enabling private conversations with documents.

Explanation of quantisation methods

Click to see details

The new methods available are:

GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw.

How to download GGUF files

Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

LM Studio
LoLLMS Web UI
Faraday.dev

In `text-generation-webui`

Under Download Model, you can enter the model repo: LiteLLMs/Llama3-OpenBioLLM-70B-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf.

Then click Download.

On the command line, including multiple files at once

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub

Then you can download any individual model file to the current directory, at high speed, with a command like this:

huggingface-cli download LiteLLMs/Llama3-OpenBioLLM-70B-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False

More advanced huggingface-cli download usage (click to read)

You can also download multiple files at once with a pattern:

huggingface-cli download LiteLLMs/Llama3-OpenBioLLM-70B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'

For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install huggingface_hub[hf_transfer]

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/Llama3-OpenBioLLM-70B-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False

Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.

## Example `llama.cpp` command

Make sure you are using llama.cpp from commit d0cee0d or later.

./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>"

Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change -c 8192 to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.

If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins

For other parameters and how to use them, please refer to the llama.cpp documentation

How to run in `text-generation-webui`

Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model Tab.md.

How to run from Python code

You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.

How to load this model in Python code, using llama-cpp-python

For full documentation, please see: llama-cpp-python docs.

First install the package

Run one of the following commands, according to your system:

# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python

Simple llama-cpp-python example code

from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
  model_path="./Q4_0/Q4_0-00001-of-00009.gguf",  # Download the model file first
  n_ctx=32768,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
  "<PROMPT>", # Prompt
  max_tokens=512,  # Generate up to 512 tokens
  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
  echo=True        # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You are a story writing assistant."},
        {
            "role": "user",
            "content": "Write a story about llamas."
        }
    ]
)

How to use with LangChain

Here are guides on using llama-cpp-python and ctransformers with LangChain:

Original model card: Llama3-OpenBioLLM-70B

Advancing Open-source Large Language Models in Medical Domain

Online Demo | GitHub | Paper | Discord

Introducing OpenBioLLM-70B: A State-of-the-Art Open Source Biomedical Large Language Model

OpenBioLLM-70B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks.

🏥 Biomedical Specialization: OpenBioLLM-70B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency.

🎓 Superior Performance: With 70 billion parameters, OpenBioLLM-70B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 on biomedical benchmarks.

🧠 Advanced Training Techniques: OpenBioLLM-70B builds upon the powerful foundations of the Meta-Llama-3-70B-Instruct and Meta-Llama-3-70B-Instruct models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include:

Policy Optimization: Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)
Fine-tuning dataset: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated)

This combination of cutting-edge techniques enables OpenBioLLM-70B to align with key capabilities and preferences for biomedical applications.

⚙️ Release Details:

Model Size: 70 billion parameters
Quantization: Optimized quantized versions available Here
Language(s) (NLP): en
Developed By: Ankit Pal (Aaditya Ura) from Saama AI Labs
License: Meta-Llama License
Fine-tuned from models: Meta-Llama-3-70B-Instruct
Resources for more information:
- Paper: Coming soon

The model can be fine-tuned for more specialized tasks and datasets as needed.

OpenBioLLM-70B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences.

We are excited to share OpenBioLLM-70B with researchers and developers around the world.

Use with transformers

Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.

See the snippet below for usage with Transformers:

import transformers
import torch

model_id = "aaditya/OpenBioLLM-Llama3-70B"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="auto",
)

messages = [
    {"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."},
    {"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"},
]

prompt = pipeline.tokenizer.apply_chat_template(
        messages, 
        tokenize=False, 
        add_generation_prompt=True
)

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipeline(
    prompt,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.0,
    top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])

Training procedure

Training hyperparameters

Click to see details

learning_rate: 0.0002
lr_scheduler: cosine
train_batch_size: 12
eval_batch_size: 8
GPU: H100 80GB SXM5
num_devices: 8
optimizer: adamw_bnb_8bit
lr_scheduler_warmup_steps: 100
num_epochs: 4

Peft hyperparameters

Click to see details

adapter: qlora
lora_r: 128
lora_alpha: 256
lora_dropout: 0.05
lora_target_linear: true

-lora_target_modules:

q_proj
v_proj
k_proj
o_proj
gate_proj
down_proj
up_proj

Training results

Framework versions

Transformers 4.39.3
Pytorch 2.1.2+cu121
Datasets 2.18.0
Tokenizers 0.15.1
Axolotl
Lm harness for evaluation

Benchmark Results

🔥 OpenBioLLM-70B demonstrates superior performance compared to larger models, such as GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 86.06%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge.

🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy.

| | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA 4 opts | PubMedQA | MedMCQA | Avg | | | - | | - | | | OpenBioLLM-70B | 92.93 | 93.197 | 83.904 | 93.75 | 93.827 | 85.749 | 78.162 | 78.97 | 74.014 | 86.05588 | | Med-PaLM-2 (5-shot) | 88.3 | 90 | 77.8 | 95.2 | 94.4 | 80.9 | 79.7 | 79.2 | 71.3 | 84.08 | | GPT-4 | 86.04 | 91 | 80 | 93.01 | 95.14 | 76.88 | 78.87 | 75.2 | 69.52 | 82.85 | | Med-PaLM-1 (Flan-PaLM, 5-shot) | 80.4 | 75 | 63.7 | 83.8 | 88.9 | 76.3 | 67.6 | 79 | 57.6 | 74.7 | | OpenBioLLM-8B | 76.101 | 86.1 | 69.829 | 78.21 | 84.213 | 68.042 | 58.993 | 74.12 | 56.913 | 72.502 | | Gemini-1.0 | 76.7 | 75.8 | 66.7 | 77.7 | 88 | 69.2 | 58 | 70.7 | 54.3 | 70.79 | | GPT-3.5 Turbo 1106 | 74.71 | 74 | 72.79 | 72.79 | 72.91 | 64.73 | 57.71 | 72.66 | 53.79 | 66 | | Meditron-70B | 66.79 | 69 | 53.33 | 71.69 | 76.38 | 63 | 57.1 | 76.6 | 46.85 | 64.52 | | gemma-7b | 69.81 | 70 | 59.26 | 66.18 | 79.86 | 60.12 | 47.21 | 76.2 | 48.96 | 64.18 | | Mistral-7B-v0.1 | 68.68 | 71 | 55.56 | 68.38 | 68.06 | 59.54 | 50.82 | 75.4 | 48.2 | 62.85 | | Apollo-7B | 62.26 | 72 | 61.48 | 69.12 | 70.83 | 55.49 | 55.22 | 39.8 | 53.77 | 60 | | MedAlpaca-7b | 57.36 | 69 | 57.04 | 67.28 | 65.28 | 54.34 | 41.71 | 72.8 | 37.51 | 58.03 | | BioMistral-7B | 59.9 | 64 | 56.5 | 60.4 | 59 | 54.7 | 50.6 | 77.5 | 48.1 | 57.3 | | AlpaCare-llama2-7b | 49.81 | 49 | 45.92 | 33.82 | 50 | 43.35 | 29.77 | 72.2 | 34.42 | 45.36 | | ClinicalGPT | 30.56 | 27 | 30.37 | 19.48 | 25 | 24.27 | 26.08 | 63.8 | 28.18 | 30.52 |

Detailed Medical Subjectwise accuracy

Use Cases & Examples

🚨 **Below results are from the quantized version of OpenBioLLM-70B

Summarize Clinical Notes

OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries

Answer Medical Questions

OpenBioLLM-70B can provide answers to a wide range of medical questions.

Click to see details

Clinical Entity Recognition

OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research.

Biomarkers Extraction

Classification

OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization

De-Identification

OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA.

Advisory Notice!

While OpenBioLLM-70B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments.

Therefore, we strongly advise against using OpenBioLLM-70B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B is intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor.

Appropriately adapting and validating OpenBioLLM-70B for specific medical use cases would require significant additional work, potentially including:

Thorough testing and evaluation in relevant clinical scenarios
Alignment with evidence-based guidelines and best practices
Mitigation of potential biases and failure modes
Integration with human oversight and interpretation
Compliance with regulatory and ethical standards

Always consult a qualified healthcare provider for personal medical needs.

Citation

If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows:

@misc{OpenBioLLMs,
  author = {Ankit Pal, Malaikannan Sankarasubbu},
  title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}}
}

The accompanying paper is currently in progress and will be released soon.

💌 Contact

We look forward to hearing you and collaborating on this exciting project!

Contributors:

Ankit Pal (Aaditya Ura) [aadityaura at gmail dot com]
Saama AI Labs
Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset.

References

We thank the Meta Team for their amazing models!

Result sources

[1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375)
[2] Med-PaLM-1 Large Language Models Encode Clinical Knowledge
[3] Med-PaLM-2 Towards Expert-Level Medical Question Answering with Large Language Models
[4] Gemini-1.0 Gemini Goes to Med School

Llama3-OpenBioLLM-70B-GGUF

Description

About GGUF

Explanation of quantisation methods

How to download GGUF files

In text-generation-webui

On the command line, including multiple files at once

How to run in text-generation-webui

How to run from Python code

How to load this model in Python code, using llama-cpp-python

First install the package

Simple llama-cpp-python example code

How to use with LangChain

Original model card: Llama3-OpenBioLLM-70B

Advancing Open-source Large Language Models in Medical Domain

Use with transformers

Training procedure

Training hyperparameters

Peft hyperparameters

Training results

Framework versions

Benchmark Results

Detailed Medical Subjectwise accuracy

Use Cases & Examples

Summarize Clinical Notes

Answer Medical Questions

Clinical Entity Recognition

Biomarkers Extraction

Classification

De-Identification

Citation

💌 Contact

References

In `text-generation-webui`

How to run in `text-generation-webui`