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StableMed is a 3 billion parameter decoder-only language model fine tuned on 18k rows of medical questions over 1 epoch.

Usage

Get started generating text with StableMed by using the following code snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("cxllin/StableMed-3b")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/stablelm-3b-4e1t",
  trust_remote_code=True,
  torch_dtype="auto",
)
model.cuda()
inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to("cuda")
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.75,
  top_p=0.95,
  do_sample=True,
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))

Model Architecture

The model is a decoder-only transformer similar to the LLaMA (Touvron et al., 2023) architecture with the following modifications:

Parameters Hidden Size Layers Heads Sequence Length
2,795,443,200 2560 32 32 4096
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Inference Examples
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Dataset used to train cxllin/StableMed-3b