metadata
language:
- en
license: apache-2.0
library_name: transformers
tags:
- code
datasets:
- Leon-Leee/wizardlm_evol_instruct_v2_196K_backuped
- m-a-p/Code-Feedback
- openbmb/UltraInteract_sft
- ise-uiuc/Magicoder-Evol-Instruct-110K
- flytech/python-codes-25k
metrics:
- code_eval
AIGCodeGeek-DS-6.7B
Introduction
AIGCodeGeek-DS-6.7B is the first released version of our Code-LLM family with competitive performance on public and private benchmarks.
Model Details
Model Description
- Developed by: Leon Li
- License: DeepSeek
- Fine-tuned from deepseek-ai/deepseek-coder-6.7b-base with full parameters
Training data
A mixture of samples from high-quality open-source (read Acknowledgements) and our private datasets. We have made contamination detection as Magicoder/Bigcode did.
Evaluation
results to be added.
Requirements
It should work with the same requirements as DeepSeek-Coder-6.7B or the following packages:
tokenizers>=0.14.0
transformers>=4.35.0
accelerate
sympy>=1.12
pebble
timeout-decorator
attrdict
QuickStart
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("aigcode/AIGCodeGeek-DS-6.7B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("aigcode/AIGCodeGeek-DS-6.7B", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
messages=[
{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
# tokenizer.eos_token_id is the id of <|EOT|> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
Limits
Acknowledgements
We gain a lot of knowledge and resources from the open-source community:
- DeepSeekCoder: impressive model series and insightful tech reports
- WizardCoder: Evol Instruct and public datasets
- We used a (Leon-Leee/wizardlm_evol_instruct_v2_196K_backuped) since this original has been deleted.
- Magicoder: OSS-Instruct, Magicoder-Evol-Instruct-110K from theblackcat102/evol-codealpaca-v1(https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1)
- Eurus: creative datasets for reasoning, openbmb/UltraInteract_sft
- OpenCoderInterpreter: well-designed system and datasets m-a-p/Code-Feedback
- flytech/python-codes-25k: diversity
- LLaMA-Factory: easily used to finetune base models