Model Type | code, instruct, self instruct |
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Use Cases |
Areas: | code instruction, code generation |
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Additional Notes | Issues with device="auto" in model arguments, requires trust_remote_code=True. |
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Supported Languages | Markdown (proficient), Java (proficient), JavaScript (proficient), Python (proficient), TypeScript (proficient), PHP (proficient), SQL (proficient), JSX (proficient), reStructuredText (proficient), Rust (proficient), C (proficient), CSS (proficient), Go (proficient), C++ (proficient), HTML (proficient), Vue (proficient), Ruby (proficient), Jupyter Notebook (proficient), R (proficient), Shell (proficient) |
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Training Details |
Data Sources: | bigcode/the-stack-dedup, sahil2801/CodeAlpaca-20k, teknium/GPTeacher-CodeInstruct |
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Data Volume: | ~25,000 code instruction/response pairs |
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Methodology: | fine-tuning using CodeAlpaca & GPTeacher datasets to add instruct capabilities |
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Training Time: | |
Hardware Used: | |
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Input Output |
Input Format: | "### Instruction: ### Input: ### Response:" or "### Instruction: ### Response:" |
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Output Format: | |
Performance Tips: | Sampler settings: max_new_tokens=128, do_sample=True, use_cache=True, temperature=0.2, top_p=0.9, eos_token_id=self.tokenizer.eos_token_id. Tokenizer decode arguments: skip_special_tokens=True, clean_up_tokenization_space=False |
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