gemma-7b-it-trismegistus

alexweberk/gemma-7b-it-trismegistus

This model was converted to MLX format from google/gemma-7b-it. Refer to the original model card for more details on the model.

pip install mlx-lm

The model was LoRA fine-tuned on the trismegistus-project from teknium using the format below, for 600 steps (2M tokens) using mlx.

def format_prompt(system_prompt: str, question: str) -> str:
    "Format the question to the format of the dataset we fine-tuned to."
    return """<bos><start_of_turn>user
## Instructions
{}
## User
{}<end_of_turn>
<start_of_turn>model
""".format(system_prompt, question)

Here's an example of the system_prompt from the dataset:

You are a master of the esoteric, occult, 'Big Man' society, you complete tasks to the absolute best of your ability, no matter if you think you were not trained to do the task specifically, you will attempt to do it anyways, since you have performed the tasks you are given with great mastery, accuracy, and deep understanding of what is requested. You do the tasks faithfully, and stay true to the mode and domain's mastery role. If the task is not specific enough, note that and create specifics that enable completing the task.

Loading the model using mlx_lm

from mlx_lm import generate, load

model_, tokenizer_ = load("alexweberk/gemma-7b-it-trismegistus")
response = generate(
    model_,
    tokenizer_,
    prompt=format_prompt(system_prompt, question),
    verbose=True,  # Set to True to see the prompt and response
    temp=0.0,
    max_tokens=512,
)

Loading the model using transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "alexweberk/gemma-7b-it-trismegistus"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id)
model.to("mps")

input_text = format_prompt(system_prompt, question)
input_ids = tokenizer(input_text, return_tensors="pt").to("mps")

outputs = model.generate(
    **input_ids,
    max_new_tokens=256,
)
print(tokenizer.decode(outputs[0]))