reattempt upload

.gitignore

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+.DS_Store

README.md

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 ---
 license: mit
+datasets:
+- jmhessel/newyorker_caption_contest
+language:
+- en
+tags:
+- nyc
+- llama2
+widget:
+- text: "This scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller.\ncaption: Can I interest you in opening an offshore account?\nexplanation of the caption:\n"
+  example_title: "Training prompt format"
+- text: "In this task, you will see a description of an uncanny situation. Then, you will see a joke that was written about the situation. Explain how the joke relates to the situation and why it is funny.\n###\nThis scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller.\ncaption: Can I interest you in opening an offshore account?\nexplanation of the caption:\n"
+  example_title: "Paper prompt format"
+- text: "This scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller.\ncaption: Can I interest you in opening an offshore account?\nthe caption is funny because"
+  example_title: "Suggested prompt format"
 ---
+
+# nyrkr-joker-llama
+
+Essentials:
+- Based on LLaMa2-7b-hf (version 2, 7B params)
+- Used [QLoRA](https://github.com/artidoro/qlora/blob/main/qlora.py) to fine-tune on [1.2k rows of New Yorker caption contest](https://huggingface.co/datasets/jmhessel/newyorker_caption_contest)
+- Merged LLaMa2 with the adapter weights (from checkpoint step=160, epoch=2.7)
+
+## Prompt options
+
+[The original paper](https://arxiv.org/abs/2209.06293), Figure 10 uses this format for joke explanations:
+
+`In this task, you will see a description of an uncanny situation. Then, you will see a joke that was written about the situation. Explain how the joke relates to the situation and why it is funny.
+###
+
+{few-shot examples separated by ###, newline after "explanation of the caption:"}
+This scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller.
+caption: Can I interest you in opening an offshore account?
+explanation of the caption:
+`
+
+In training, I used just the individual example:
+
+`This scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller.
+caption: Can I interest you in opening an offshore account?
+explanation of the caption:\n`
+
+In inference, I had some better results with a more natural prompt (no newline or space at end)
+
+`This scene takes place in the following location: a bank. Three people are standing in line at the bank. The bank teller is a traditional pirate with a hook hand, eye patch, and a parrot. The scene includes: Piracy, Bank teller.
+caption: Can I interest you in opening an offshore account?
+the caption is funny because`
+
+## Training script
+
+Trained on a V100
+
+```
+git clone https://github.com/artidoro/qlora
+cd qlora
+
+pip3 install -r requirements.txt --quiet
+
+! cd qlora && python qlora.py \
+    --model_name_or_path ../llama-2-7b-hf \
+    --output_dir ../thatsthejoke \
+    --logging_steps 20 \
+    --save_strategy steps \
+    --data_seed 42 \
+    --save_steps 80 \
+    --save_total_limit 10 \
+    --evaluation_strategy steps \
+    --max_new_tokens 64 \
+    --dataloader_num_workers 1 \
+    --group_by_length \
+    --logging_strategy steps \
+    --remove_unused_columns False \
+    --do_train \
+    --lora_r 64 \
+    --lora_alpha 16 \
+    --lora_modules all \
+    --double_quant \
+    --quant_type nf4 \
+    --bits 4 \
+    --warmup_ratio 0.03 \
+    --lr_scheduler_type constant \
+    --gradient_checkpointing \
+    --dataset /content/nycaptions.jsonl \
+    --dataset_format 'self-instruct' \
+    --source_max_len 16 \
+    --target_max_len 512 \
+    --per_device_train_batch_size 1 \
+    --gradient_accumulation_steps 16 \
+    --max_steps 250 \
+    --eval_steps 187 \
+    --learning_rate 0.0002 \
+    --adam_beta2 0.999 \
+    --max_grad_norm 0.3 \
+    --lora_dropout 0.1 \
+    --weight_decay 0.0 \
+    --seed 0
+```

config.json

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+{
+  "_name_or_path": "monsoon-nlp/nyrkr-joker-llama",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.31.0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
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+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.31.0"
+}

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