wehub-resource-sync
104 lines
2.6 KiB
YAML
104 lines
2.6 KiB
YAML
defaults:
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- hydra: default
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- _self_
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hydra:
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searchpath:
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- file://conf/
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train_file: "openai_humaneval"
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dev_file: ${train_file}
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test_file: ${train_file}
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port: 6000
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model: ds-coder-v1.5-chat
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sampling_params:
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_target_: vllm.SamplingParams
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n: 1
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temperature: 0.0
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stop: [ "</s>", "\n\n\n\n", "Context:\n", "Thought 42:", "<|end_of_text|>", "<|eot_id|>, <|EOT|>" ]
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max_tokens: 4096
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tem: ${sampling_params.temperature}
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n: ${sampling_params.n}
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split_size: -1
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split_id: 0
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max_num_seqs: 32
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output_file: ${output_dir}/human_eval/${eval_sub_path}/test.0shot.tem${tem}.n${n}.v2.2.json
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flush_file: ${output_file}l
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apply_chat_template: True
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add_generation_prompt: True
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#chat_prefix: "<｜begin▁of▁sentence｜>You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\n### Instruction:\n"
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#chat_connect: "\n### Response:\n"
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#chat_suffix: "\n<|EOT|>"
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prompt: "Complete the following Python function:\n\n{prompt}\n\nPlease put your code in code block\n```python\n...\n```\nDo not change any code in the function head and do completion only."
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# Data loading
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read_tensor:
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_target_: data.combine_dataset.ResponseAlignDataset
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read_fn:
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_target_: data.human_eval.HumanEvalReader
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template: ${prompt}
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instruction:
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index_field: "task_id"
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service_based: False
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split_size: ${split_size}
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split_id: ${split_id}
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service_processor:
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_target_: data.vllm.VLLMRequestGenerator
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api_url: http://0.0.0.0:${port}/v1/completions
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max_tokens: 4096
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model: ${model}
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stop: ${sampling_params.stop}
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temperature: ${sampling_params.temperature}
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n: ${sampling_params.n}
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max_data_num: -1
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save_best: False
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eval_sub_path:
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output_dir: ../pretrained-models/deepseek-coder-7b-instruct-v1.5/
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# Dataloader
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num_workers: 32
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prefetch_factor: 2
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dp_size:
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tp_size: 1
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pp_size: 1
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post_process:
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_target_: post_processors.code.code.CodeExtractor
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output_file: ${output_file}
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answer_clean:
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_target_: post_processors.code.clean.get
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name: standard_default
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index_field: task_id
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test_case_field: "test"
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evaluator:
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_target_: post_processors.code.evaluator.HumanEvaluator
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saved_keys: [ "prompt", "entry_point" ]
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num_workers: 16
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# Training hyper-parameters
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per_gpu_train_batch_size: 1
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per_gpu_eval_batch_size: 1
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ddp_eval: False
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no_cuda: False
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seed: 42
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local_rank: -1
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# Temporary variables
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fp16: True
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fp16_bfloat16: True
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n_gpu: 1
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device:
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train_batch_size:
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eval_batch_size:
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world_size: |