This repo contains PMC_LLaMA_7B, which is LLaMA-7b finetuned on the PMC papers in S2ORC dataset.

The model was trained with the following hyperparameters:

* Epochs: 5 
* Batch size: 128 
* Cutoff length: 512
* Learning rate: 2e-5

Each epoch we sample 512 tokens per paper for training.

The model can be loaded as following:

```
import transformers
import torch
tokenizer = transformers.LlamaTokenizer.from_pretrained('chaoyi-wu/PMC_LLAMA_7B')
model = transformers.LlamaForCausalLM.from_pretrained('chaoyi-wu/PMC_LLAMA_7B')
sentence = 'Hello, doctor' 
batch = tokenizer(
            sentence,
            return_tensors="pt", 
            add_special_tokens=False
        )
with torch.no_grad():
    generated = model.generate(inputs = batch["input_ids"], max_length=200, do_sample=True, top_k=50)
    print('model predict: ',tokenizer.decode(generated[0]))
```
{"license": "apache-2.0", "tags": ["medical"], "datasets": ["allenai/s2orc"]}
transformers pytorch llama text-generation medical dataset:allenai/s2orc license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #llama #text-generation #medical #dataset-allenai/s2orc #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

This repo contains PMC_LLaMA_7B, which is LLaMA-7b finetuned on the PMC papers in S2ORC dataset.

The model was trained with the following hyperparameters:

* Epochs: 5 
* Batch size: 128 
* Cutoff length: 512
* Learning rate: 2e-5

Each epoch we sample 512 tokens per paper for training.

The model can be loaded as following:


TAGS
#transformers #pytorch #llama #text-generation #medical #dataset-allenai/s2orc #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

TAGS
#transformers #pytorch #llama #text-generation #medical #dataset-allenai/s2orc #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
