--- base_model: Writer/Palmyra-Med-70B tags: - fp8 - vllm - medical - med license: other license_name: writer-open-model-license license_link: https://writer.com/legal/open-model-license/ language: - en --- # Palmyra-Medical-70B-FP8 This is a quantized version of [Palmyra-Med-70B](https://huggingface.co/Writer/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](https://writer.com/legal/open-model-license/) ### Writer Resources and Technical Documentation: + [Writer Blog](https://writer.com/blog/palmyra-med-fin-models/) + [Writer Developer Website](https://dev.writer.com/home/models) + [Writer AI Studio](https://writer.com/product/ai-studio/) + [Palmyra Model API](https://dev.writer.com/api-guides/chat-completion) ### Model Optimizations [LLM_Compressor](https://github.com/vllm-project/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](https://docs.vllm.ai/en/latest/) library, as shown in the example below. ```python 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](https://github.com/vllm-project/llm-compressor/blob/sa/big_model_support/examples/big_model_offloading/big_model_w8a8_calibrate.py), as presented in the code below. ```python 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
Biomedical Benchmark Med-PaLM-2 (5-shot) GPT-4 Palmyra-Med-70B (Original FP16) Palmyra-Medical-70B-FP8 (This Model)
MMLU Clincal Knowledge 88.3 86.0 90.9 90.2
MMLU Medical Genetics 90.0 91.0 94.0 93.0
MMLU Anatomy 77.8 80.0 83.7 83.7
MMLU Professional Medicine 95.2 93.0 92.7 92.3
MMLU College Biology 94.4 95.1 94.4 93.8
MMLU College Medicine 80.9 76.9 84.4 84.4
MedQA 4-options 79.9 78.9 78.6 79.5
PubMed QA 79.2 75.2 79.6 78.0
MedMCQA 71.3 69.5 74.4 75.7
Average 84.1 82.8 85.9 85.6
### 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 } ```