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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- medical |
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- palmyra |
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--- |
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**DEPRECATED MODEL NOTICE** |
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========================== |
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Please note that this model is no longer maintained or supported by our team. We strongly advise against using it in production or for any critical applications. |
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Instead, we recommend using our latest and greatest models, which can be found at: |
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https://huggingface.co/collections/Writer/palmyra-writer-license-66476fa8156169f8720a2c89 |
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========================== |
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# Palmyra-med-20b |
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## Model description |
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**Palmyra-Med-20b** is a 20 billion parameter Large Language Model that has been uptrained on |
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**Palmyra-Large** with a specialized custom-curated medical dataset. |
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The main objective of this model is to enhance performance in tasks related to medical dialogue |
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and question-answering. |
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- **Developed by:** [https://writer.com/](https://writer.com/); |
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- **Model type:** Causal decoder-only; |
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- **Language(s) (NLP):** English; |
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- **License:** Apache 2.0; |
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- **Finetuned from model:** [Palmyra-Large](https://huggingface.co/Writer/palmyra-large). |
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### Model Source |
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[Palmyra-Med: Instruction-Based Fine-Tuning of LLMs Enhancing Medical Domain Performance](https://dev.writer.com/docs/palmyra-med-instruction-based-fine-tuning-of-llms-enhancing-medical-domain-performance) |
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## Uses |
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### Out-of-Scope Use |
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Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. |
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## Bias, Risks, and Limitations |
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Palmyra-Med-20B is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. |
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### Recommendations |
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We recommend users of Palmyra-Med-20B to develop guardrails and to take appropriate precautions for any production use. |
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## Usage |
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The model is compatible with the huggingface `AutoModelForCausalLM` and can be easily run on a single 40GB A100. |
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```py |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_name = "Writer/palmyra-med-20b" |
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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device_map="auto", |
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torch_dtype=torch.float16, |
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) |
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prompt = "Can you explain in simple terms how vaccines help our body fight diseases?" |
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input_text = ( |
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"A chat between a curious user and an artificial intelligence assistant. " |
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"The assistant gives helpful, detailed, and polite answers to the user's questions. " |
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"USER: {prompt} " |
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"ASSISTANT:" |
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) |
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model_inputs = tokenizer(input_text.format(prompt=prompt), return_tensors="pt").to( |
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"cuda" |
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) |
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gen_conf = { |
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"temperature": 0.7, |
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"repetition_penalty": 1.0, |
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"max_new_tokens": 512, |
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"do_sample": True, |
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} |
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out_tokens = model.generate(**model_inputs, **gen_conf) |
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response_ids = out_tokens[0][len(model_inputs.input_ids[0]) :] |
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output = tokenizer.decode(response_ids, skip_special_tokens=True) |
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print(output) |
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## output ## |
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# Vaccines stimulate the production of antibodies by the body's immune system. |
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# Antibodies are proteins produced by B lymphocytes in response to foreign substances,such as viruses and bacteria. |
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# The antibodies produced by the immune system can bind to and neutralize the pathogens, preventing them from invading and damaging the host cells. |
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# Vaccines work by introducing antigens, which are components of the pathogen, into the body. |
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# The immune system then produces antibodies against the antigens, which can recognize and neutralize the pathogen if it enters the body in the future. |
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# The use of vaccines has led to a significant reduction in the incidence and severity of many diseases, including measles, mumps, rubella, and polio. |
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``` |
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It can also be used with text-generation-inference |
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```sh |
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model=Writer/palmyra-med-20b |
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volume=$PWD/data |
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference --model-id $model |
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``` |
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## Dataset |
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For the fine-tuning of our LLMs, we used a custom-curated medical dataset that combines data from |
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two publicly available sources: PubMedQA (Jin et al. 2019) and MedQA (Zhang et al. 2018).The |
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PubMedQA dataset, which originated from the PubMed abstract database, consists of biomedical |
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articles accompanied by corresponding question-answer pairs. In contrast, the MedQA dataset |
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features medical questions and answers that are designed to assess the reasoning capabilities of |
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medical question-answering systems. |
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We prepared our custom dataset by merging and processing data from the aforementioned sources, |
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maintaining the dataset mixture ratios detailed in Table 1. These ratios were consistent for finetuning |
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both Palmyra-20b and Palmyra-40b models. Upon fine-tuning the models with this dataset, we refer |
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to the resulting models as Palmyra-Med-20b and Palmyra-Med-40b, respectively. |
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| Dataset | Ratio | Count | |
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| -----------|----------- | ----------- | |
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| PubMedQA | 75% | 150,000 | |
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| MedQA | 25% | 10,178 | |
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## Evaluation |
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we present the findings of our experiments, beginning with the evaluation outcomes of |
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the fine-tuned models and followed by a discussion of the base models’ performance on each of the |
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evaluation datasets. Additionally, we report the progressive improvement of the Palmyra-Med-40b |
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model throughout the training process on the PubMedQA dataset. |
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| Model | PubMedQA | MedQA | |
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| -----------|----------- | ----------- | |
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| Palmyra-20b | 49.8 | 31.2 | |
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| Palmyra-40b | 64.8 | 43.1| |
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| Palmyra-Med-20b| 75.6 | 44.6| |
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| Palmyra-Med-40b| 81.1 | 72.4| |
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## Limitation |
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The model may not operate efficiently beyond the confines of the healthcare field. |
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Since it has not been subjected to practical scenarios, its real-time efficacy and precision remain undetermined. |
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Under no circumstances should it replace the advice of a medical professional, and it must be regarded solely as a tool for research purposes. |
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## Citation and Related Information |
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To cite this model: |
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``` |
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@misc{Palmyra-Med-20B, |
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author = {Writer Engineering team}, |
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title = {{Palmyra-Large Parameter Autoregressive Language Model}}, |
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howpublished = {\url{https://dev.writer.com}}, |
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year = 2023, |
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month = March |
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} |
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``` |
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## Contact |
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[email protected] |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Writer__palmyra-med-20b) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 40.02 | |
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| ARC (25-shot) | 46.93 | |
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| HellaSwag (10-shot) | 73.51 | |
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| MMLU (5-shot) | 44.34 | |
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| TruthfulQA (0-shot) | 35.47 | |
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| Winogrande (5-shot) | 65.35 | |
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| GSM8K (5-shot) | 2.65 | |
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| DROP (3-shot) | 11.88 | |
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