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"| Step | \n", "Training Loss | \n", "
|---|---|
| 1 | \n", "3.611500 | \n", "
| 2 | \n", "3.613000 | \n", "
| 3 | \n", "3.488000 | \n", "
| 4 | \n", "3.600900 | \n", "
| 5 | \n", "3.031900 | \n", "
| 6 | \n", "2.884400 | \n", "
| 7 | \n", "2.446000 | \n", "
| 8 | \n", "2.323400 | \n", "
| 9 | \n", "1.984300 | \n", "
| 10 | \n", "1.647600 | \n", "
| 11 | \n", "1.613200 | \n", "
| 12 | \n", "1.516100 | \n", "
| 13 | \n", "1.629300 | \n", "
| 14 | \n", "1.463600 | \n", "
| 15 | \n", "1.500300 | \n", "
| 16 | \n", "1.439500 | \n", "
| 17 | \n", "1.375500 | \n", "
| 18 | \n", "1.322500 | \n", "
| 19 | \n", "1.352800 | \n", "
| 20 | \n", "1.394700 | \n", "
| 21 | \n", "1.527800 | \n", "
| 22 | \n", "1.386900 | \n", "
| 23 | \n", "1.570800 | \n", "
| 24 | \n", "1.209500 | \n", "
| 25 | \n", "1.345200 | \n", "
| 26 | \n", "1.388600 | \n", "
| 27 | \n", "1.416400 | \n", "
| 28 | \n", "1.392500 | \n", "
| 29 | \n", "1.375700 | \n", "
| 30 | \n", "1.252900 | \n", "
" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "cellView": "form", "id": "pCqnaKmlO1U9", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "888d5a4b-7c0d-485a-d347-efaaed70abe5" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "957.9653 seconds used for training.\n", "15.97 minutes used for training.\n", "Peak reserved memory = 10.287 GB.\n", "Peak reserved memory for training = 1.81 GB.\n", "Peak reserved memory % of max memory = 69.752 %.\n", "Peak reserved memory for training % of max memory = 12.273 %.\n" ] } ], "source": [ "#@title Show final memory and time stats\n", "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", "used_percentage = round(used_memory /max_memory*100, 3)\n", "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n", "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n", "print(f\"Peak reserved memory = {used_memory} GB.\")\n", "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "ekOmTR1hSNcr" }, "source": [ "\n", "### Inference\n", "Let's run the model! You can change the instruction and input - leave the output blank!\n", "\n", "We use `min_p = 0.1` and `temperature = 1.5`. Read this [Tweet](https://x.com/menhguin/status/1826132708508213629) for more information on why." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "kR3gIAX-SM2q", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "ac901efd-de05-40ab-ac45-45c0f35ad9ff" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Panoramic radiography shows multiple small osteolytic lesions in the right mandibular ramus, condyle and ascending ramus, the left mandibular condyle, left first molar and body, right first and second premolar, left first, second and third molars (arrows).<|eot_id|>\n" ] } ], "source": [ "FastVisionModel.for_inference(model) # Enable for inference!\n", "\n", "image = dataset[0][\"image\"]\n", "instruction = \"You are an expert radiographer. Describe accurately what you see in this image.\"\n", "\n", "messages = [\n", " {\"role\": \"user\", \"content\": [\n", " {\"type\": \"image\"},\n", " {\"type\": \"text\", \"text\": instruction}\n", " ]}\n", "]\n", "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt = True)\n", "inputs = tokenizer(\n", " image,\n", " input_text,\n", " add_special_tokens = False,\n", " return_tensors = \"pt\",\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n", " use_cache = True, temperature = 1.5, min_p = 0.1)" ] }, { "cell_type": "markdown", "metadata": { "id": "uMuVrWbjAzhc" }, "source": [ "\n", "### Saving, loading finetuned models\n", "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", "\n", "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "upcOlWe7A1vc", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "f70e56fe-3cf1-4980-b259-ae2de09e824a" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, "metadata": {}, "execution_count": 17 } ], "source": [ "model.save_pretrained(\"lora_model\") # Local saving\n", "tokenizer.save_pretrained(\"lora_model\")\n", "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" ] }, { "cell_type": "markdown", "metadata": { "id": "AEEcJ4qfC7Lp" }, "source": [ "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "MKX_XKs_BNZR", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "d9c5f139-6084-431a-acc1-62f4d5d248ad" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Panoramic radiograph of the jaw showing well-defined radiolucency (arrowheads) of the right side and unerupted second permanent maxillary molar (arrow).<|eot_id|>\n" ] } ], "source": [ "if False:\n", " from unsloth import FastVisionModel\n", " model, tokenizer = FastVisionModel.from_pretrained(\n", " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", " load_in_4bit = load_in_4bit,\n", " )\n", " FastVisionModel.for_inference(model) # Enable for inference!\n", "\n", "image = dataset[0][\"image\"]\n", "instruction = \"You are an expert radiographer. Describe accurately what you see in this image.\"\n", "\n", "messages = [\n", " {\"role\": \"user\", \"content\": [\n", " {\"type\": \"image\"},\n", " {\"type\": \"text\", \"text\": instruction}\n", " ]}\n", "]\n", "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt = True)\n", "inputs = tokenizer(\n", " image,\n", " input_text,\n", " add_special_tokens = False,\n", " return_tensors = \"pt\",\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n", " use_cache = True, temperature = 1.5, min_p = 0.1)" ] }, { "cell_type": "markdown", "metadata": { "id": "f422JgM9sdVT" }, "source": [ "### Saving to float16 for VLLM\n", "\n", "We also support saving to `float16` directly. Select `merged_16bit` for float16. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "iHjt_SMYsd3P" }, "outputs": [], "source": [ "# Select ONLY 1 to save! (Both not needed!)\n", "\n", "# Save locally to 16bit\n", "if False: model.save_pretrained_merged(\"unsloth_finetune\", tokenizer,)\n", "\n", "# To export and save to your Hugging Face account\n", "if False: model.push_to_hub_merged(\"YOUR_USERNAME/unsloth_finetune\", tokenizer, token = \"PUT_HERE\")" ] }, { "cell_type": "markdown", "metadata": { "id": "Zt9CHJqO6p30" }, "source": [ "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", "\n", "Some other links:\n", "1. Llama 3.2 Conversational notebook. [Free Colab](https://colab.research.google.com/drive/1T5-zKWM_5OD21QHwXHiV9ixTRR7k3iB9?usp=sharing)\n", "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)\n", "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/drive/1j0N4XTY1zXXy7mPAhOC1_gMYZ2F2EBlk?usp=sharing)\n", "4. Qwen 2 VL Vision finetuning - Maths OCR to LaTeX. [Free Colab](https://colab.research.google.com/drive/1whHb54GNZMrNxIsi2wm2EY_-Pvo2QyKh?usp=sharing)\n", "5. Pixtral 12B Vision finetuning - General QA datasets. [Free Colab](https://colab.research.google.com/drive/1K9ZrdwvZRE96qGkCq_e88FgV3MLnymQq?usp=sharing)\n", "6. More notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [Github](https://github.com/unslothai/unsloth)!\n", "\n", "
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