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--- |
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base_model: Writer/Palmyra-Med-70B |
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tags: |
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- fp8 |
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- vllm |
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- medical |
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- med |
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license: other |
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license_name: writer-open-model-license |
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license_link: https://writer.com/legal/open-model-license/ |
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language: |
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- en |
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--- |
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# Palmyra-Medical-70B-FP8 |
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This is a quantized version of [Palmyra-Med-70B](https://huggingface.co/Writer/Palmyra-Med-70B), which was developed by Writer. |
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The original model performance on biomedical benchmarks is 85.87%. |
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**This quantized version acheives an average score of 85.62%.** |
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## Model Overview: |
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- **Model:** Llama based model finetuned to form Palmyra-X-004 and then again to form Palmyra-Med-70B. |
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- **Input:** Text |
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- **Output:** Text |
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- **Model Optimizations:** |
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- **Weight quantization:** FP8 |
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- **Activation quantization:** FP8 |
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- **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. |
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- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. |
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- **License(s):** [writer-open-model-license](https://writer.com/legal/open-model-license/) |
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### Writer Resources and Technical Documentation: |
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+ [Writer Blog](https://writer.com/blog/palmyra-med-fin-models/) |
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+ [Writer Developer Website](https://dev.writer.com/home/models) |
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+ [Writer AI Studio](https://writer.com/product/ai-studio/) |
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+ [Palmyra Model API](https://dev.writer.com/api-guides/chat-completion) |
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### Model Optimizations |
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[LLM_Compressor](https://github.com/vllm-project/llm-compressor) library. |
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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. |
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## Deployment with vLLM |
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This model can be deployed using the [vLLM](https://docs.vllm.ai/en/latest/) library, as shown in the example below. |
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```python |
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from vllm import LLM, SamplingParams |
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from transformers import AutoTokenizer |
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model_id = "bprice9/Palmyra-Medical-70B-FP8" |
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number_gpus = 2 |
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sampling_params = SamplingParams(temperature=0.0, top_p=0.9, max_tokens=512, stop_token_ids=[128001, 128009]) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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messages = [ |
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{"role": "user", "content": "Give a differential for an intrahepatic lesion with early arterial phase enhancement and rapid washout."}, |
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] |
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prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) |
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llm = LLM(model=model_id, tensor_parallel_size=number_gpus) |
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outputs = llm.generate(prompts, sampling_params) |
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generated_text = outputs[0].outputs[0].text |
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print(generated_text) |
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``` |
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## Creation |
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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. |
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```python |
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import torch |
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from datasets import load_dataset |
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from transformers import AutoTokenizer |
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from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot |
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from llmcompressor.transformers.compression.helpers import ( |
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calculate_offload_device_map, |
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custom_offload_device_map, |
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) |
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recipe = """ |
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quant_stage: |
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quant_modifiers: |
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QuantizationModifier: |
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ignore: ["lm_head"] |
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config_groups: |
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group_0: |
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weights: |
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num_bits: 8 |
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type: float |
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strategy: tensor |
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dynamic: false |
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symmetric: true |
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input_activations: |
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num_bits: 8 |
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type: float |
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strategy: tensor |
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dynamic: false |
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symmetric: true |
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targets: ["Linear"] |
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""" |
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model_stub = "Writer/Palmyra-Med-70B" |
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model_name = model_stub.split("/")[-1] |
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device_map = calculate_offload_device_map( |
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model_stub, reserve_for_hessians=False, num_gpus=2, torch_dtype=torch.float16 |
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) |
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model = SparseAutoModelForCausalLM.from_pretrained( |
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model_stub, torch_dtype=torch.float16, device_map=device_map |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_stub) |
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output_dir = f"./{model_name}-FP8" |
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DATASET_ID = "HuggingFaceH4/ultrachat_200k" |
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DATASET_SPLIT = "train_sft" |
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NUM_CALIBRATION_SAMPLES = 128 |
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MAX_SEQUENCE_LENGTH = 4096 |
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ds = load_dataset(DATASET_ID, split=DATASET_SPLIT) |
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ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES)) |
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def preprocess(example): |
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return { |
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"text": tokenizer.apply_chat_template( |
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example["messages"], |
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tokenize=False, |
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) |
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} |
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ds = ds.map(preprocess) |
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def tokenize(sample): |
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return tokenizer( |
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sample["text"], |
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padding=False, |
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max_length=MAX_SEQUENCE_LENGTH, |
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truncation=True, |
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add_special_tokens=False, |
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) |
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ds = ds.map(tokenize, remove_columns=ds.column_names) |
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oneshot( |
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model=model, |
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output_dir=output_dir, |
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dataset=ds, |
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recipe=recipe, |
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max_seq_length=MAX_SEQUENCE_LENGTH, |
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num_calibration_samples=NUM_CALIBRATION_SAMPLES, |
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save_compressed=True, |
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) |
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``` |
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## Evaluation |
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<table> |
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<tr> |
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<td style="width: 20%;"><strong>Biomedical Benchmark</strong> |
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</td> |
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<td style="width: 20%;"><strong>Med-PaLM-2 (5-shot)</strong> |
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</td> |
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<td style="width: 20%;"><strong>GPT-4</strong> |
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</td> |
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<td style="width: 20%;"><strong>Palmyra-Med-70B (Original FP16)</strong> |
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</td> |
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<td style="width: 20%;"><strong>Palmyra-Medical-70B-FP8 (This Model)</strong> |
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</td> |
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</tr> |
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<tr> |
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<td>MMLU Clincal Knowledge |
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</td> |
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<td>88.3 |
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</td> |
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<td>86.0 |
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</td> |
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<td>90.9 |
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</td> |
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<td>90.2 |
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</td> |
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</tr> |
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<tr> |
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<td>MMLU Medical Genetics |
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</td> |
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<td>90.0 |
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</td> |
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<td>91.0 |
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</td> |
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<td>94.0 |
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</td> |
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<td>93.0 |
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</td> |
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</tr> |
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<tr> |
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<td>MMLU Anatomy |
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</td> |
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<td>77.8 |
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</td> |
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<td>80.0 |
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</td> |
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<td>83.7 |
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</td> |
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<td>83.7 |
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</td> |
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</tr> |
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<tr> |
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<td>MMLU Professional Medicine |
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</td> |
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<td>95.2 |
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</td> |
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<td>93.0 |
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</td> |
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<td>92.7 |
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</td> |
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<td>92.3 |
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</td> |
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</tr> |
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<tr> |
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<td>MMLU College Biology |
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</td> |
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<td>94.4 |
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</td> |
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<td>95.1 |
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</td> |
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<td>94.4 |
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</td> |
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<td>93.8 |
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</td> |
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</tr> |
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<tr> |
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<td>MMLU College Medicine |
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</td> |
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<td>80.9 |
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</td> |
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<td>76.9 |
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</td> |
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<td>84.4 |
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</td> |
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<td>84.4 |
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</td> |
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</tr> |
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<tr> |
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<td>MedQA 4-options |
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</td> |
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<td>79.9 |
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</td> |
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<td>78.9 |
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</td> |
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<td>78.6 |
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</td> |
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<td>79.5 |
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</td> |
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</tr> |
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<tr> |
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<td>PubMed QA |
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</td> |
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<td>79.2 |
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</td> |
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<td>75.2 |
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</td> |
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<td>79.6 |
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</td> |
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<td>78.0 |
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</td> |
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</tr> |
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<tr> |
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<tr> |
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<td>MedMCQA |
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</td> |
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<td>71.3 |
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</td> |
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<td>69.5 |
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</td> |
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<td>74.4 |
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</td> |
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<td>75.7 |
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</td> |
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</tr> |
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<tr> |
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<td><strong>Average</strong> |
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</td> |
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<td><strong>84.1</strong> |
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</td> |
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<td><strong>82.8</strong> |
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</td> |
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<td><strong>85.9</strong> |
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</td> |
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<td><strong>85.6</strong> |
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</td> |
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</tr> |
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</table> |
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### Citation and Related Information Provided by Writer |
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To cite this model: |
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``` |
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@misc{Palmyra-Med-70B, |
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author = {Writer Engineering team}, |
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title = {{Palmyra-Med-70b: A powerful LLM designed for healthcare}}, |
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howpublished = {\url{https://dev.writer.com}}, |
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year = 2024, |
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month = June |
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} |
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``` |
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