EpistemeAI/Athena-codegemma-2-9b-v1

How to use

This repository contains Athena-codegemma-2-9b-v1, for use with transformers and with the original llama codebase.

Use with transformers Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.

Make sure to update your transformers installation via pip install --upgrade transformers.

Best use to test or prompt:

You need to prepare prompt in alpaca format to generate properly:

Basic

f"""Below is an instruction that describes a task. \
    Write a response that appropriately completes the request.

    ### Instruction:
    {x['instruction']}

    ### Input:
    {x['input']}

    ### Response:
    """

Here is example:

def format_test(x):

  if x['input']:
    formatted_text = f"""Below is an instruction that describes a task. \
    Write a response that appropriately completes the request.

    ### Instruction:
    {x['instruction']}

    ### Input:
    {x['input']}

    ### Response:
    """

  else:
    formatted_text = f"""Below is an instruction that describes a task. \
    Write a response that appropriately completes the request.

    ### Instruction:
    {x['instruction']}

    ### Response:
    """

  return formatted_text

# using code_instructions_122k_alpaca dataset
Prompt = format_test(data[155])
print(Prompt)

• huggingface transformers method:

from transformers import TextStreamer

FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(
[
    Prompt
], return_tensors = "pt").to("cuda")

text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 512)

• unsloth method

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "EpistemeAI/Athena-codegemma-2-9b-v1", # YOUR MODEL YOU USED FOR TRAINING
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference

# alpaca_prompt = You MUST copy from above!

inputs = tokenizer(
[
    alpaca_prompt.format(
        "Create a function to calculate the sum of a sequence of integers.", # instruction
        "", # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
tokenizer.batch_decode(outputs)

--

Inputs and outputs

• Input: Text string, such as a question, a prompt, or a document to be summarized.
• Output: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document.

Citation

@article{gemma_2024,
    title={Gemma},
    url={https://www.kaggle.com/m/3301},
    DOI={10.34740/KAGGLE/M/3301},
    publisher={Kaggle},
    author={Gemma Team},
    year={2024}
}

Uploaded model

• Developed by: EpistemeAI
• License: apache-2.0
• Finetuned from model : EpistemeAI/Athena-codegemma-2-9b

This gemma2 model was trained 2x faster with Unsloth and Huggingface's TRL library.