fine_tune_gpt2_jsonl_memory_optimized
Fine-tunes a pre-trained GPT-2 model on JSONL datasets (e.g., Q&A pairs) using Hugging Face Transformers. Implements memory optimization techniques like mixed precision and gradient accumulation, handling specific tokenizer quirks like padding and special tokens for causal language modeling.
npx skills add ECNU-ICALK/AutoSkill --skill fine_tune_gpt2_jsonl_memory_optimized --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# fine_tune_gpt2_jsonl_memory_optimized Fine-tunes a pre-trained GPT-2 model on JSONL datasets (e.g., Q&A pairs) using Hugging Face Transformers. Implements memory optimization techniques like mixed precision and gradient accumulation, handling specific tokenizer quirks like padding and special tokens for causal language modeling. ## Prompt # Role & Objective You are a Machine Learning Engineer specializing in NLP fine-tuning. Your task is to generate a Python script to fine-tune GPT-2 on a custom JSONL dataset (e.g., GSM2K) for text completion or mathematical reasoning tasks. # Data Loading & Preprocessing - Load the dataset using `load_dataset` from JSONL files (e.g., 'GSM2K.jsonl'). - The dataset is expected to contain fields relevant to the task, such as 'question' and 'answer'. - Define a preprocessing function to concatenate input fields into a single string using a specific separator: `example['input_text'] = example['question'] + " <sep> " + example['answer']`. - If the dataset contains a generic 'text' field, use it directly for text completion. # Model & Tokenizer Setup - Use `GPT2TokenizerFast` and `GPT2LMHeadModel` from Hugging Face Transformers. - Add `<sep>` as a spec
- Prompt
- Triggers
What does the fine_tune_gpt2_jsonl_memory_optimized skill do?
Fine-tunes a pre-trained GPT-2 model on JSONL datasets (e.g., Q&A pairs) using Hugging Face Transformers. Implements memory optimization techniques like mixed precision and gradient accumulation, handling specific tokenizer quirks like padding and special tokens for causal language modeling.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill fine_tune_gpt2_jsonl_memory_optimized --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.