GGML f16, q4_0, q4_1, q4_2, q4_3
I converted and quantized these with https://github.com/ggerganov/ggml/ on a MacBook pro M1 w/ 16GB RAM.
https://huggingface.co/oeathus/stablelm-tuned-alpha-7b-ggml-q4
Can you give me a heads up on how to plug these in and perform some local inference on my mac? Here is what I have so far:
def hugging_local(text="Can you please let us know more details about your "):
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-tuned-alpha-7b")
model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-tuned-alpha-7b")
from langchain.llms import HuggingFacePipeline
llm = HuggingFacePipeline(model=model, tokenizer=tokenizer)
template = """Question: {question}
Answer: """
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "Who won the FIFA World Cup in the year 1994? "
print(llm_chain.run(question))
return
if __name__ == '__main__':
# result = hugging_lang()
# result = hugging_raw(text=test_text)
result = hugging_local(text=test_text)
print(result)
I'm still wrapping my head around the GGML format. My understanding is that it is a custom serialized binary format that sorta zips the parameters and other essentials on top of the actual neural net. I don't think you can run these with the Hugging Face transformers library, but I'm not terribly confident about that.
Okay, yeah, I am struggling. I also was trying to use the hosted inference and it just times out constantly.
ldilov/stablelm-tuned-alpha-7b-4bit-128g-descact-sym-true-sequential