2487 lines
78 KiB
Plaintext
2487 lines
78 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
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"<div class=\"align-center\">\n",
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"<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
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"<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
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"<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a> Join Discord if you need help + \u2b50 <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\n",
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"</div>\n",
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"\n",
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"To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n",
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"\n",
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"You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### News"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"[Vision RL](https://docs.unsloth.ai/new/vision-reinforcement-learning-vlm-rl) is now supported! Train Qwen2.5-VL, Gemma 3 etc. with GSPO or GRPO.\n",
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"\n",
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"Introducing Unsloth [Standby for RL](https://docs.unsloth.ai/basics/memory-efficient-rl): GRPO is now faster, uses 30% less memory with 2x longer context.\n",
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"\n",
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"Gpt-oss fine-tuning now supports 8\u00d7 longer context with 0 accuracy loss. [Read more](https://docs.unsloth.ai/basics/long-context-gpt-oss-training)\n",
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"\n",
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"Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).\n",
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"\n",
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"Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Installation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": "%%capture\n# We're installing the latest Torch, Triton, OpenAI's Triton kernels, Transformers and Unsloth!\n!pip install --upgrade -qqq uv\ntry: import numpy; get_numpy = f\"numpy=={numpy.__version__}\"\nexcept: get_numpy = \"numpy\"\n!uv pip install -qqq \\\n \"torch>=2.8.0\" \"triton>=3.4.0\" {get_numpy} torchvision bitsandbytes \"transformers>=4.55.3\" \\\n \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n \"unsloth[base] @ git+https://github.com/unslothai/unsloth\" \\\n git+https://github.com/triton-lang/triton.git@05b2c186c1b6c9a08375389d5efe9cb4c401c075#subdirectory=python/triton_kernels\n!uv pip install transformers==4.55.4\n!uv pip install --no-deps trl==0.22.2"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%capture\n",
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"!uv pip install --force-reinstall --no-deps git+https://github.com/unslothai/unsloth-zoo\n",
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"!uv pip install --force-reinstall --no-deps git+https://github.com/unslothai/unsloth"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Unsloth"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "r2v_X2fA0Df5"
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},
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"source": [
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"We're about to demonstrate the power of the new OpenAI GPT-OSS 20B model through an inference example. For our `MXFP4` version, use this [notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_(20B)-Inference.ipynb) instead."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 371,
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"referenced_widgets": [
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]
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},
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"id": "QmUBVEnvCDJv",
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"outputId": "14ca09b9-e1ff-4f91-b98c-7a1ed22eef3a"
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "7ca2facea2414549ab2a0a3fd07a0723",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"config.json: 0%| | 0.00/1.15k [00:00<?, ?B/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"==((====))== Unsloth: Fast Llama patching release 2024.4\n",
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" \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
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"O^O/ \\_/ \\ Pytorch: 2.2.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
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"\\ / Bfloat16 = FALSE. Xformers = 0.0.25.post1. FA = False.\n",
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" \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Unused kwargs: ['_load_in_4bit', '_load_in_8bit', 'quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "d9c5466214ed4f06876a89c64c29048f",
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"version_major": 2,
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"text/plain": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "2abdab3fdd4f4a70a8faaa7b0e39f811",
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"version_major": 2,
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "36250ac65fcb4756a4368f98902a1617",
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"version_major": 2,
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"metadata": {},
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "ebaa8d22074d495ea675f51dc2d5a4d6",
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"version_major": 2,
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"version_minor": 0
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "932920c0ee834e3e8ffe53ebf7833fa7",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
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"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
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]
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}
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],
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"source": [
|
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"from unsloth import FastLanguageModel\n",
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"import torch\n",
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"\n",
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"# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
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"fourbit_models = [\n",
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" \"unsloth/gpt-oss-20b-unsloth-bnb-4bit\", # 20B model using bitsandbytes 4bit quantization\n",
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" \"unsloth/gpt-oss-120b-unsloth-bnb-4bit\",\n",
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" \"unsloth/gpt-oss-20b\", # 20B model using MXFP4 format\n",
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" \"unsloth/gpt-oss-120b\", \n",
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"] # More models at https://huggingface.co/unsloth\n",
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"\n",
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"model, tokenizer = FastLanguageModel.from_pretrained(\n",
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" model_name = \"unsloth/gpt-oss-20b-unsloth-bnb-4bit\",\n",
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" dtype = None, # None for auto detection\n",
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" max_seq_length = 4096, # Choose any for long context!\n",
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" load_in_4bit = False, # 4 bit quantization to reduce memory\n",
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" full_finetuning = False, # [NEW!] We have full finetuning now!\n",
|
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" # token = \"hf_...\", # use one if using gated models\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Reasoning Effort\n",
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"The `gpt-oss` models from OpenAI include a feature that allows users to adjust the model's \"reasoning effort.\" This gives you control over the trade-off between the model's performance and its response speed (latency) which by the amount of token the model will use to think.\n",
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"\n",
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"----\n",
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"\n",
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"The `gpt-oss` models offer three distinct levels of reasoning effort you can choose from:\n",
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"\n",
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"* **Low**: Optimized for tasks that need very fast responses and don't require complex, multi-step reasoning.\n",
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"* **Medium**: A balance between performance and speed.\n",
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"* **High**: Provides the strongest reasoning performance for tasks that require it, though this results in higher latency."
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]
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},
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{
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"cell_type": "code",
|
|
"execution_count": null,
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|
"metadata": {},
|
|
"outputs": [],
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"source": [
|
|
"from transformers import TextStreamer\n",
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"\n",
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"messages = [\n",
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" {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n",
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"]\n",
|
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"inputs = tokenizer.apply_chat_template(\n",
|
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" messages,\n",
|
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" add_generation_prompt = True,\n",
|
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" return_tensors = \"pt\",\n",
|
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" return_dict = True,\n",
|
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" reasoning_effort = \"low\", # **NEW!** Set reasoning effort to low, medium or high\n",
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").to(model.device)\n",
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"\n",
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"_ = model.generate(**inputs, max_new_tokens = 512, streamer = TextStreamer(tokenizer))"
|
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]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Changing the `reasoning_effort` to `medium` will make the model think longer. We have to increase the `max_new_tokens` to occupy the amount of the generated tokens but it will give better and more correct answer"
|
|
]
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from transformers import TextStreamer\n",
|
|
"\n",
|
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"messages = [\n",
|
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" {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n",
|
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"]\n",
|
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"inputs = tokenizer.apply_chat_template(\n",
|
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" messages,\n",
|
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" add_generation_prompt = True,\n",
|
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" return_tensors = \"pt\",\n",
|
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" return_dict = True,\n",
|
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" reasoning_effort = \"medium\", # **NEW!** Set reasoning effort to low, medium or high\n",
|
|
").to(model.device)\n",
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"\n",
|
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"_ = model.generate(**inputs, max_new_tokens = 1024, streamer = TextStreamer(tokenizer))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Lastly we will test it using `reasoning_effort` to `high`"
|
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]
|
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from transformers import TextStreamer\n",
|
|
"\n",
|
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"messages = [\n",
|
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" {\"role\": \"user\", \"content\": \"Solve x^5 + 3x^4 - 10 = 3.\"},\n",
|
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"]\n",
|
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"inputs = tokenizer.apply_chat_template(\n",
|
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" messages,\n",
|
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" add_generation_prompt = True,\n",
|
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" return_tensors = \"pt\",\n",
|
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" return_dict = True,\n",
|
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" reasoning_effort = \"high\", # **NEW!** Set reasoning effort to low, medium or high\n",
|
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").to(model.device)\n",
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"\n",
|
|
"_ = model.generate(**inputs, max_new_tokens = 2048, streamer = TextStreamer(tokenizer))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
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"metadata": {},
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"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. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n",
|
|
"2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
|
|
"3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
|
|
"6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n",
|
|
"\n",
|
|
"<div class=\"align-center\">\n",
|
|
" <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
|
|
" <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
|
|
" <a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
|
|
"\n",
|
|
" Join Discord if you need help + \u2b50\ufe0f <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> \u2b50\ufe0f\n",
|
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"</div>\n"
|
|
]
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}
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