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"| Step | \n", "Training Loss | \n", "
|---|---|
| 1 | \n", "3.606200 | \n", "
| 2 | \n", "3.559900 | \n", "
| 3 | \n", "3.455600 | \n", "
| 4 | \n", "3.590300 | \n", "
| 5 | \n", "3.043800 | \n", "
| 6 | \n", "2.893200 | \n", "
| 7 | \n", "2.378200 | \n", "
| 8 | \n", "2.236700 | \n", "
| 9 | \n", "1.899900 | \n", "
| 10 | \n", "1.583200 | \n", "
| 11 | \n", "1.553700 | \n", "
| 12 | \n", "1.440600 | \n", "
| 13 | \n", "1.525500 | \n", "
| 14 | \n", "1.335200 | \n", "
| 15 | \n", "1.389600 | \n", "
| 16 | \n", "1.304200 | \n", "
| 17 | \n", "1.230500 | \n", "
| 18 | \n", "1.186900 | \n", "
| 19 | \n", "1.204900 | \n", "
| 20 | \n", "1.282200 | \n", "
| 21 | \n", "1.406500 | \n", "
| 22 | \n", "1.303800 | \n", "
| 23 | \n", "1.474400 | \n", "
| 24 | \n", "1.083900 | \n", "
| 25 | \n", "1.266300 | \n", "
| 26 | \n", "1.276500 | \n", "
| 27 | \n", "1.307900 | \n", "
| 28 | \n", "1.309800 | \n", "
| 29 | \n", "1.293300 | \n", "
| 30 | \n", "1.161600 | \n", "
" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "pCqnaKmlO1U9", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "9d308986-99b3-4b3c-d4f6-514a4e2ae821" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "969.6321 seconds used for training.\n", "16.16 minutes used for training.\n", "Peak reserved memory = 10.693 GB.\n", "Peak reserved memory for training = 1.625 GB.\n", "Peak reserved memory % of max memory = 72.505 %.\n", "Peak reserved memory for training % of max memory = 11.018 %.\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": null, "metadata": { "id": "kR3gIAX-SM2q", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "505ebdec-7c14-4892-ddcc-e2ffb67e0af6" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Panoramic radiography shows multiple small osteolytic lesions in the right mandibular ramus, condyle and ascending ramus area as well as the bilateral maxillar sinuses (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": null, "metadata": { "id": "upcOlWe7A1vc", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "928e9c9d-8a22-49f4-e800-67ee1ffef7e5" }, "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": null, "metadata": { "id": "MKX_XKs_BNZR", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "63cda882-1d84-433c-eab8-e7c49b5ae56f" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The panoramic radiograph shows bilateral hypoplasia of the condylar heads and mandibular fossae in a patient with fibrodysplasia ossificans progressiva.<|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 = True, # Set to False for 16bit LoRA\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": null, "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/unsloth) 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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