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updated .gitignore for ch07/01 artefacts (#242)
* fixed markdown * removed redundant imports * updated .gitignore for ch07/01 artefacts
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Daniel Kleine
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.gitignore
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.gitignore
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@ -18,6 +18,10 @@ ch06/01_main-chapter-code/accuracy-plot.pdf
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ch07/01_main-chapter-code/loss-plot.pdf
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ch07/01_main-chapter-code/loss-plot-standalone.pdf
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ch07/01_main-chapter-code/loss-plot-baseline.pdf
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ch07/01_main-chapter-code/loss-plot-mask-instructions.pdf
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ch07/01_main-chapter-code/loss-plot-phi3-prompt.pdf
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ch07/01_main-chapter-code/loss-plot-alpaca52k.pdf
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# Checkpoint files
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appendix-A/01_main-chapter-code/model.pth
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@ -34,6 +38,11 @@ ch06/01_main-chapter-code/gpt2
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ch06/02_bonus_additional-experiments/gpt2
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ch06/03_bonus_imdb-classification/gpt2
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ch07/01_main-chapter-code/gpt2-medium355M-sft-baseline.pth
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ch07/01_main-chapter-code/gpt2-medium355M-sft-mask-instructions.pth
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ch07/01_main-chapter-code/gpt2-medium355M-sft-phi3-prompt.pth
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ch07/01_main-chapter-code/gpt2-medium355M-sft-alpaca52k.pth
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ch07/01_main-chapter-code/gpt2-medium355M-sft-lora.pth
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ch07/01_main-chapter-code/gpt2-medium355M-sft.pth
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ch07/01_main-chapter-code/gpt2-medium355M-sft-standalone.pth
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ch07/01_main-chapter-code/Smalltestmodel-sft-standalone.pth
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@ -71,6 +80,10 @@ ch06/03_bonus_imdb-classification/validation.csv
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ch07/01_main-chapter-code/instruction-data-with-response-standalone.json
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ch07/01_main-chapter-code/instruction-data-with-response-baseline.json
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ch07/01_main-chapter-code/instruction-data-with-response-mask-instructions.json
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ch07/01_main-chapter-code/loss-plot-lora.pdf
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ch07/01_main-chapter-code/instruction-data-with-response-alpaca52k.json
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ch07/01_main-chapter-code/instruction-data-with-response-lora.json
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ch07/01_main-chapter-code/instruction-data-with-response-phi3-prompt.json
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ch07/02_dataset-utilities/instruction-examples-modified.json
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# Temporary OS-related files
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@ -43,7 +43,7 @@
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"source": [
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"Suppose we have the following data entry:\n",
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"\n",
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"```\n",
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"```json\n",
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"{\n",
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" \"instruction\": \"Identify the correct spelling of the following word.\",\n",
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" \"input\": \"Ocassion\",\n",
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@ -195,24 +195,24 @@
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"metadata": {},
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"source": [
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"```python\n",
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" for i, entry in tqdm(enumerate(test_data), total=len(test_data)):\n",
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"for i, entry in tqdm(enumerate(test_data), total=len(test_data)):\n",
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"\n",
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" input_text = format_input(entry)\n",
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" tokenizer=tokenizer\n",
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" input_text = format_input(entry)\n",
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" tokenizer=tokenizer\n",
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"\n",
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" token_ids = generate(\n",
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" model=model,\n",
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" idx=text_to_token_ids(input_text, tokenizer).to(device),\n",
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" max_new_tokens=256,\n",
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" context_size=BASE_CONFIG[\"context_length\"],\n",
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" eos_id=50256\n",
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" )\n",
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" generated_text = token_ids_to_text(token_ids, tokenizer)\n",
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" token_ids = generate(\n",
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" model=model,\n",
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" idx=text_to_token_ids(input_text, tokenizer).to(device),\n",
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" max_new_tokens=256,\n",
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" context_size=BASE_CONFIG[\"context_length\"],\n",
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" eos_id=50256\n",
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" )\n",
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" generated_text = token_ids_to_text(token_ids, tokenizer)\n",
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"\n",
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" # New: Adjust ###Response -> <|assistant|>\n",
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" response_text = generated_text[len(input_text):].replace(\"<|assistant|>:\", \"\").strip()\n",
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" # New: Adjust ###Response -> <|assistant|>\n",
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" response_text = generated_text[len(input_text):].replace(\"<|assistant|>:\", \"\").strip()\n",
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"\n",
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" test_data[i][\"model_response\"] = response_text\n",
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" test_data[i][\"model_response\"] = response_text\n",
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"```"
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]
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},
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@ -229,7 +229,7 @@
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"id": "dd8158e9-cc70-4e0f-88b0-73c3e1d8c030",
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"metadata": {},
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"source": [
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"```python\n",
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"```bash\n",
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"python exercise_experiments.py --exercise_solution phi3_prompt\n",
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"```\n",
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"\n",
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@ -273,7 +273,7 @@
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"\n",
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"Let's take a look at some of the responses to make sure they have been formatted correctly:\n",
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"\n",
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"```\n",
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"```json\n",
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" {\n",
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" \"instruction\": \"Rewrite the sentence using a simile.\",\n",
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" \"input\": \"The car is very fast.\",\n",
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@ -296,7 +296,7 @@
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"\n",
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"We can evaluate the performance using the Ollama Llama 3 method, which is for your convenience, also implemented in the `python exercise_experiments.py` script, which we can run as follows:\n",
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"\n",
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"```python\n",
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"```bash\n",
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"python ollama_evaluate.py --file_path instruction-data-with-response-phi3-prompt.json\n",
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"```\n",
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"\n",
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@ -309,7 +309,7 @@
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"Average score: 48.87\n",
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"```\n",
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"\n",
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"The score is close to 50, which is in the same ballpark as the score we previously achieved with the Alpaca-style prompts.\n"
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"The score is close to 50, which is in the same ballpark as the score we previously achieved with the Alpaca-style prompts."
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]
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},
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{
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@ -802,7 +802,7 @@
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"id": "be9ab66f-5819-4b01-9a03-c45aa3b7c5b8",
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"metadata": {},
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"source": [
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"```\n",
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"```json\n",
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"[\n",
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" {\n",
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" \"instruction\": \"Edit the following sentence to increase readability: \\\"He made a huge effort and was so successful.\\\"\",\n",
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@ -832,7 +832,7 @@
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"source": [
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"Finally, we can evaluate the finetuned LLM using the [ollama_evaluate.py](ollama_evaluate.py) utility function:\n",
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"\n",
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"```\n",
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"```bash\n",
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"python ollama_evaluate.py --file_path instruction-data-with-response-alpaca52k.json\n",
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"```\n",
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"\n",
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@ -867,7 +867,7 @@
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"To instruction finetune the model using LoRA, use the relevant classes and functions from appendix E:\n",
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"\n",
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"```python\n",
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" from appendix_E import LoRALayer, LinearWithLoRA, replace_linear_with_lora\n",
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"from appendix_E import LoRALayer, LinearWithLoRA, replace_linear_with_lora\n",
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"```"
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]
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},
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@ -961,7 +961,7 @@
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"\n",
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"We can evaluate the performance using the Ollama Llama 3 method, which is for your convenience, also implemented in the `python exercise_experiments.py` script, which we can run as follows:\n",
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"\n",
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"```python\n",
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"```bash\n",
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"python ollama_evaluate.py --file_path instruction-data-with-response-lora.json\n",
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"```\n",
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"\n",
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@ -994,7 +994,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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"version": "3.10.11"
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}
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},
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"nbformat": 4,
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@ -86,8 +86,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from gpt_download import download_and_load_gpt2\n",
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"from previous_chapters import GPTModel, load_weights_into_gpt\n",
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"from previous_chapters import GPTModel\n",
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"\n",
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"\n",
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"BASE_CONFIG = {\n",
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