metadata

base_model: EpistemeAI/Athene-codegemma-2-7b-it-alpaca-v1
language:
  - en
license: apache-2.0
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - gemma
  - trl

Model Information

Summary description and brief definition of inputs and outputs.

Description

Athene CodeGemma 2 7B v1.1 is a collection of lightweight open code models built on top of Gemma. CodeGemma models are text-to-text and text-to-code decoder-only models and are available as a 7 billion pretrained variant that specializes in code completion and code generation tasks, a 7 billion parameter instruction-tuned variant for code chat and instruction following and a 2 billion parameter pretrained variant for fast code completion. Supervised Fine-tuning with coding datasets.

similar to:

	codegemma-2b	codegemma-7b	codegemma-7b-it
Code Completion	✅	✅
Generation from natural language		✅	✅
Chat			✅
Instruction Following			✅

Sample Usage

This model is intended to answer questions about code fragments, to generate code from natural language, or to engage in a conversation with the user about programming or technical problems. If you need to use code completion (for example, integrated in an IDE), we recommend you use one of the pre-trained models instead: CodeGemma 7B, or CodeGemma 2B.

For Code Generation

from transformers import GemmaTokenizer, AutoModelForCausalLM
tokenizer = GemmaTokenizer.from_pretrained("EpistemeAI/Athene-codegemma-7b-it-alpaca-v1.1")
model = AutoModelForCausalLM.from_pretrained("EpistemeAI/Athene-codegemma-7b-it-alpaca-v1.1")
input_text = "Write me a Python function to calculate the nth fibonacci number."
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))

Chat Template

The instruction-tuned models use a chat template that must be adhered to for conversational use. The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.

Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "EpistemeAI/Athene-codegemma-2-7b-it-alpaca-v1"
dtype = torch.bfloat16
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="cuda",
    torch_dtype=dtype,
)
chat = [
    { "role": "user", "content": "Write a hello world program" },
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)

At this point, the prompt contains the following text:

<bos><start_of_turn>user
Write a hello world program<end_of_turn>
<start_of_turn>model

As you can see, each turn is preceded by a <start_of_turn> delimiter and then the role of the entity (either user, for content supplied by the user, or model for LLM responses). Turns finish with the <end_of_turn> token.

You can follow this format to build the prompt manually, if you need to do it without the tokenizer's chat template.

After the prompt is ready, generation can be performed like this:

inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)

Inputs and Outputs

Inputs : For pretrained model variants: code prefix and/or suffix for code completion and generation scenarios, or natural language text or prompt : For instruction tuned model variant: natural language text or prompt

Outputs : For pretrained model variants: fill-in-the-middle code completion, code and natural language : For instruction tuned model variant: code and natural language

Model Data

Data used for model training and how the data was processed.

Training Dataset

Using Gemma as the base model, Athene CodeGemma 2 7B pretrained variants are further trained on an additional 500 billion tokens of primarily English language data from publicly available code repositories, open source mathematics datasets and synthetically generated code.

Uploaded model

Developed by: EpistemeAI
License: apache-2.0
Finetuned from model : EpistemeAI/Athene-codegemma-2-7b-it-alpaca-v1

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