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README.md
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---
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license name: deepseek
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library_name: transformers
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tags:
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- code
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metrics:
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- code_eval
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pipeline_tag: text-generation
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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
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### Model Details
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#### Model Description
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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.
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### Evaluation
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results to be added.
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### QuickStart
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```python
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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
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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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print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
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```
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### Limits
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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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---
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library_name: transformers
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tags:
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- code
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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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### 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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### 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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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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