Palmyra-Medical-70B-FP8

Palmyra-Medical-70B-FP8

This is a quantized version of Palmyra-Med-70B, which was developed by Writer.

The original model performance on biomedical benchmarks is 85.87%. This quantized version acheives an average score of 85.62%.

Model Overview:

• Model: Llama based model finetuned to form Palmyra-X-004 and then again to form Palmyra-Med-70B.
  • Input: Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: FP8
  • Activation quantization: FP8
• Intended Use Cases: Palmyra-Medical-70B-FP8 is intended for non-commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
• Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
• License(s): writer-open-model-license

Writer Resources and Technical Documentation:

• Writer Blog
• Writer Developer Website
• Writer AI Studio
• Palmyra Model API

Model Optimizations

LLM_Compressor library. Using this optimization, the original FP16 weights and linear activations within the transformer blocks are adjusted to FP8, which decreases the model size and VRAM requirements by 50% overall.

Deployment with vLLM

This model can be deployed using the vLLM library, as shown in the example below.

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "bprice9/Palmyra-Medical-70B-FP8"
number_gpus = 2

sampling_params = SamplingParams(temperature=0.0, top_p=0.9, max_tokens=512, stop_token_ids=[128001, 128009])

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [
    {"role": "user", "content": "Give a differential for an intrahepatic lesion with early arterial phase enhancement and rapid washout."},
]
prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

llm = LLM(model=model_id, tensor_parallel_size=number_gpus)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

Creation

This model was created by applying LLM Compressor with calibration samples from UltraChat, as presented in the code below.

import torch
from datasets import load_dataset
from transformers import AutoTokenizer
from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
from llmcompressor.transformers.compression.helpers import (
    calculate_offload_device_map,
    custom_offload_device_map,
)
recipe = """
quant_stage:
    quant_modifiers:
        QuantizationModifier:
            ignore: ["lm_head"]
            config_groups:
                group_0:
                    weights:
                        num_bits: 8
                        type: float
                        strategy: tensor
                        dynamic: false
                        symmetric: true
                    input_activations:
                        num_bits: 8
                        type: float
                        strategy: tensor
                        dynamic: false
                        symmetric: true
                    targets: ["Linear"]
"""
model_stub = "Writer/Palmyra-Med-70B"
model_name = model_stub.split("/")[-1]
device_map = calculate_offload_device_map(
    model_stub, reserve_for_hessians=False, num_gpus=2, torch_dtype=torch.float16
)
model = SparseAutoModelForCausalLM.from_pretrained(
    model_stub, torch_dtype=torch.float16, device_map=device_map
)
tokenizer = AutoTokenizer.from_pretrained(model_stub)
output_dir = f"./{model_name}-FP8"
DATASET_ID = "HuggingFaceH4/ultrachat_200k"
DATASET_SPLIT = "train_sft"
NUM_CALIBRATION_SAMPLES = 128
MAX_SEQUENCE_LENGTH = 4096
ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
def preprocess(example):
    return {
        "text": tokenizer.apply_chat_template(
            example["messages"],
            tokenize=False,
        )
    }
ds = ds.map(preprocess)
def tokenize(sample):
    return tokenizer(
        sample["text"],
        padding=False,
        max_length=MAX_SEQUENCE_LENGTH,
        truncation=True,
        add_special_tokens=False,
    )
ds = ds.map(tokenize, remove_columns=ds.column_names)
oneshot(
    model=model,
    output_dir=output_dir,
    dataset=ds,
    recipe=recipe,
    max_seq_length=MAX_SEQUENCE_LENGTH,
    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
    save_compressed=True,
)

Evaluation

<table> <tr> <td style="width: 20%;"><strong>Biomedical Benchmark</strong> </td> <td style="width: 20%;"><strong>Med-PaLM-2 (5-shot)</strong> </td> <td style="width: 20%;"><strong>GPT-4</strong> </td> <td style="width: 20%;"><strong>Palmyra-Med-70B (Original FP16)</strong> </td> <td style="width: 20%;"><strong>Palmyra-Medical-70B-FP8 (This Model)</strong> </td> </tr> <tr> <td>MMLU Clincal Knowledge </td> <td>88.3 </td> <td>86.0 </td> <td>90.9 </td> <td>90.2 </td> </tr> <tr> <td>MMLU Medical Genetics </td> <td>90.0 </td> <td>91.0 </td> <td>94.0 </td> <td>93.0 </td> </tr> <tr> <td>MMLU Anatomy </td> <td>77.8 </td> <td>80.0 </td> <td>83.7 </td> <td>83.7 </td> </tr> <tr> <td>MMLU Professional Medicine </td> <td>95.2 </td> <td>93.0 </td> <td>92.7 </td> <td>92.3 </td> </tr> <tr> <td>MMLU College Biology </td> <td>94.4 </td> <td>95.1 </td> <td>94.4 </td> <td>93.8 </td> </tr> <tr> <td>MMLU College Medicine </td> <td>80.9 </td> <td>76.9 </td> <td>84.4 </td> <td>84.4 </td> </tr> <tr> <td>MedQA 4-options </td> <td>79.9 </td> <td>78.9 </td> <td>78.6 </td> <td>79.5 </td> </tr> <tr> <td>PubMed QA </td> <td>79.2 </td> <td>75.2 </td> <td>79.6 </td> <td>78.0 </td> </tr> <tr> <tr> <td>MedMCQA </td> <td>71.3 </td> <td>69.5 </td> <td>74.4 </td> <td>75.7 </td> </tr> <tr> <td><strong>Average</strong> </td> <td><strong>84.1</strong> </td> <td><strong>82.8</strong> </td> <td><strong>85.9</strong> </td> <td><strong>85.6</strong> </td> </tr> </table>

Citation and Related Information Provided by Writer

To cite this model:

@misc{Palmyra-Med-70B,
  author = {Writer Engineering team},
  title = {{Palmyra-Med-70b: A powerful LLM designed for healthcare}},
  howpublished = {\url{https://dev.writer.com}},
  year = 2024,
  month = June 
}