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