README.md

metadata

language:
  - en
tags:
  - causal-lm
license: cc-by-nc-sa-4.0
datasets:
  - dmayhem93/ChatCombined
  - tatsu-lab/alpaca
  - nomic-ai/gpt4all_prompt_generations
  - Dahoas/full-hh-rlhf
  - jeffwan/sharegpt_vicuna
  - HuggingFaceH4/databricks_dolly_15k

StableLM-Tuned-Alpha 16-bit

Model Description

16-bit version of StableLM-Tuned-Alpha compressed for the sake of speed and memory usage. No other changes were made. Original model: https://huggingface.co/stabilityai/stablelm-tuned-alpha-7b

Usage

Get started chatting with StableLM-Tuned-Alpha 16-bit by using the following code snippet:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList
tokenizer = AutoTokenizer.from_pretrained("vvsotnikov/stablelm-tuned-alpha-7b-16bit")
model = AutoModelForCausalLM.from_pretrained("vvsotnikov/stablelm-tuned-alpha-7b-16bit", torch_dtype=torch.float16)
model.cuda()
class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [50278, 50279, 50277, 1, 0]
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False
system_prompt = """<|SYSTEM|># StableLM Tuned (Alpha version)
- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
- StableLM will refuse to participate in anything that could harm a human.
"""
prompt = f"{system_prompt}<|USER|>What's your mood today?<|ASSISTANT|>"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
tokens = model.generate(
  **inputs,
  max_new_tokens=64,
  temperature=0.7,
  do_sample=True,
  stopping_criteria=StoppingCriteriaList([StopOnTokens()])
)
print(tokenizer.decode(tokens[0], skip_special_tokens=True))