metadata
tags:
- code
- gemma
library_name: transformers
pipeline_tag: text-generation
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
CodeGemma
We've fine-tuned Gemma-2b with an additional 0.7 billion high-quality, code-related tokens for 3 epochs. We used DeepSpeed ZeRO 3 and Flash Attention 2 to accelerate the training process. It achieves 54.9 pass@1 on HumanEval-Python. This model operates using the Alpaca instruction format (excluding the system prompt).
Usage
Here give some examples of how to use our model:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
PROMPT = """### Instruction
{instruction}
### Response
"""
instruction = <Your code instruction here>
prompt = PROMPT.format(instruction=instruction)
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CodeGemma-2b")
model = AutoModelForCausalLM.from_pretrained(
"TechxGenus/CodeGemma-2b",
torch_dtype=torch.bfloat16,
device_map="auto",
)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=2048)
print(tokenizer.decode(outputs[0]))
With text-generation pipeline:
from transformers import pipeline
import torch
PROMPT = """<bos>### Instruction
{instruction}
### Response
"""
instruction = <Your code instruction here>
prompt = PROMPT.format(instruction=instruction)
generator = pipeline(
model="TechxGenus/CodeGemma-2b",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(prompt, max_length=2048)
print(result[0]["generated_text"])
Note
Model may sometimes make errors, produce misleading contents, or struggle to manage tasks that are not related to coding. It has undergone very limited testing. Additional safety testing should be performed before any real-world deployments.