Upload Copy_of_Alpaca_+_Llama_3_8b_full_example.ipynb

Copy_of_Alpaca_+_Llama_3_8b_full_example.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
+        "<div class=\"align-center\">\n",
+        "  <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
+        "  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
+        "  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Join Discord if you need help + ⭐ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐\n",
+        "</div>\n",
+        "\n",
+        "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n",
+        "\n",
+        "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp).\n",
+        "\n",
+        "**[NEW] Llama-3 8b is trained on a crazy 15 trillion tokens! Llama-2 was 2 trillion.**"
+      ],
+      "metadata": {
+        "id": "IqM-T1RTzY6C"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2eSvM9zX_2d3"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
+        "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
+        "!pip install --no-deps \"xformers<0.0.26\" trl peft accelerate bitsandbytes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n",
+        "* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\n",
+        "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n",
+        "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n",
+        "* [**NEW**] With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models."
+      ],
+      "metadata": {
+        "id": "r2v_X2fA0Df5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QmUBVEnvCDJv"
+      },
+      "outputs": [],
+      "source": [
+        "from unsloth import FastLanguageModel\n",
+        "import torch\n",
+        "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
+        "dtype = None # Noad_in_4bit = True # Use 4bit quanbe False.\n",
+        "\n",
+        "# 4bit pre quantized models w no OOMs.\n",
+        "fourbit_models = [\n",
+        "    \"unslothistral-7b-instruct-v0.2-bnb-4bit\",\n",
+        "    \"unsloth/llama-2-7b-bnb-4bit\",\n",
+        "    \"unsloth/gemma-7b-bnb-4bit\",\n",
+        "  nstruct version of Gemma 7b\n",
+        "    \"unsloth/gemma-2b-bnb-4bit\",\n",
+        "    \"unsloth/gemma-2 Gemma 2b\n",
+        "    \"unsloth/llama-3-8b-bnb-4bit\", # [NEW] 15 Trillion token Llama-3\n",
+        "] # More models at https://huggingface.co/unslodel.from_pretrained(\n",
+        "    model_name = \"unsloth/llama-3-8b-bnb-4bit\",\n",
+        "    max_seq_length = max_seq_length,\n",
+        "    dtype = dtype,\n",
+        "    load_in_4bit = load_in_4bit,\n",
+        "    token = \"\"\"\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
+      ],
+      "metadata": {
+        "id": "SXd9bTZd1aaL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6bZsfBuZDeCL"
+      },
+      "outputs": [],
+      "source": [
+        "model = FastLanguageModel.get_peft_model(\n",
+        "    model,\n",
+        "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
+        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
+        "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
+        "    lora_alpha = 16,\n",
+        "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
+        "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
+        "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
+        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
+        "    random_state = 3407,\n",
+        "    use_rslora = False,  # We support rank stabilized LoRA\n",
+        "    loftq_config = None, # And LoftQ\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"Data\"></a>\n",
+        "### Data Prep\n",
+        "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n",
+        "\n",
+        "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n",
+        "\n",
+        "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n",
+        "\n",
+        "If you want to use the `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).\n",
+        "\n",
+        "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)."
+      ],
+      "metadata": {
+        "id": "vITh0KVJ10qX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LjY75GoYUCB8"
+      },
+      "outputs": [],
+      "source": [
+        "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
+        "\n",
+        "### Instruction:\n",
+        "{}\n",
+        "\n",
+        "### Input:\n",
+        "{}\n",
+        "\n",
+        "### Response:\n",
+        "{}\"\"\"\n",
+        "\n",
+        "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
+        "def formatting_prompts_func(examples):\n",
+        "    instructions = examples[\"instruction\"]\n",
+        "    inputs       = examples[\"input\"]\n",
+        "    outputs      = examples[\"output\"]\n",
+        "    texts = []\n",
+        "    for instruction, input, output in zip(instructions, inputs, outputs):\n",
+        "        # Must add EOS_TOKEN, otherwise your generation will go on forever!\n",
+        "        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n",
+        "        texts.append(text)\n",
+        "    return { \"text\" : texts, }\n",
+        "pass\n",
+        "\n",
+        "from datasets import load_dataset\n",
+        "dataset = load_dataset(\"yahma/alpaca-cleaned\", split = \"train\")\n",
+        "dataset = dataset.map(formatting_prompts_func, batched = True,)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"Train\"></a>\n",
+        "### Train the model\n",
+        "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"
+      ],
+      "metadata": {
+        "id": "idAEIeSQ3xdS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "95_Nn-89DhsL"
+      },
+      "outputs": [],
+      "source": [
+        "from trl import SFTTrainer\n",
+        "from transformers import TrainingArguments\n",
+        "\n",
+        "trainer = SFTTrainer(\n",
+        "    model = model,\n",
+        "    tokenizer = tokenizer,\n",
+        "    train_dataset = dataset,\n",
+        "    dataset_text_field = \"text\",\n",
+        "    max_seq_length = max_seq_length,\n",
+        "    dataset_num_proc = 2,\n",
+        "    packing = False, # Can make training 5x faster for short sequences.\n",
+        "    args = TrainingArguments(\n",
+        "        per_device_train_batch_size = 2,\n",
+        "        gradient_accumulation_steps = 4,\n",
+        "        warmup_steps = 5,\n",
+        "        max_steps = 60,\n",
+        "        learning_rate = 2e-4,\n",
+        "        fp16 = not torch.cuda.is_bf16_supported(),\n",
+        "        bf16 = torch.cuda.is_bf16_supported(),\n",
+        "        logging_steps = 1,\n",
+        "        optim = \"adamw_8bit\",\n",
+        "        weight_decay = 0.01,\n",
+        "        lr_scheduler_type = \"linear\",\n",
+        "        seed = 3407,\n",
+        "        output_dir = \"outputs\",\n",
+        "    ),\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2ejIt2xSNKKp",
+        "cellView": "form"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Show current memory stats\n",
+        "gpu_stats = torch.cuda.get_device_properties(0)\n",
+        "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
+        "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
+        "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
+        "print(f\"{start_gpu_memory} GB of memory reserved.\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yqxqAZ7KJ4oL"
+      },
+      "outputs": [],
+      "source": [
+        "trainer_stats = trainer.train()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pCqnaKmlO1U9",
+        "cellView": "form"
+      },
+      "outputs": [],
+      "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",
+      "source": [
+        "<a name=\"Inference\"></a>\n",
+        "### Inference\n",
+        "Let's run the model! You can change the instruction and input - leave the output blank!"
+      ],
+      "metadata": {
+        "id": "ekOmTR1hSNcr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# alpaca_prompt = Copied from above\n",
+        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
+        "inputs = tokenizer(\n",
+        "[\n",
+        "    alpaca_prompt.format(\n",
+        "        \"Continue the fibonnaci sequence.\", # instruction\n",
+        "        \"1, 1, 2, 3, 5, 8\", # input\n",
+        "        \"\", # output - leave this blank for generation!\n",
+        "    )\n",
+        "], return_tensors = \"pt\").to(\"cuda\")\n",
+        "\n",
+        "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
+        "tokenizer.batch_decode(outputs)"
+      ],
+      "metadata": {
+        "id": "kR3gIAX-SM2q"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!"
+      ],
+      "metadata": {
+        "id": "CrSvZObor0lY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# alpaca_prompt = Copied from above\n",
+        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
+        "inputs = tokenizer(\n",
+        "[\n",
+        "    alpaca_prompt.format(\n",
+        "        \"Continue the fibonnaci sequence.\", # instruction\n",
+        "        \"1, 1, 2, 3, 5, 8\", # input\n",
+        "        \"\", # output - leave this blank for generation!\n",
+        "    )\n",
+        "], return_tensors = \"pt\").to(\"cuda\")\n",
+        "\n",
+        "from transformers import TextStreamer\n",
+        "text_streamer = TextStreamer(tokenizer)\n",
+        "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
+      ],
+      "metadata": {
+        "id": "e2pEuRb1r2Vg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<a name=\"Save\"></a>\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!"
+      ],
+      "metadata": {
+        "id": "uMuVrWbjAzhc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#model.save_pretrained(\"lora_model\") # Local saving\n",
+        "#tokenizer.save_pretrained(\"lora_model\")\n",
+        "model.push_to_hub(\"Arun1982/LLama3-LoRA\", token = \"\"\"\") # Online saving\n",
+        "tokenizer.push_to_hub(\"Arun1982/LLama3-LoRA\", token = \"\"\"\") # Online saving"
+      ],
+      "metadata": {
+        "id": "upcOlWe7A1vc"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
+      ],
+      "metadata": {
+        "id": "AEEcJ4qfC7Lp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if False:\n",
+        "    from unsloth import FastLanguageModel\n",
+        "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
+        "        model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
+        "        max_seq_length = max_seq_length,\n",
+        "        dtype = dtype,\n",
+        "        load_in_4bit = load_in_4bit,\n",
+        "    )\n",
+        "    FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
+        "\n",
+        "# alpaca_prompt = You MUST copy from above!\n",
+        "\n",
+        "inputs = tokenizer(\n",
+        "[\n",
+        "    alpaca_prompt.format(\n",
+        "        \"What is a famous tall tower in Paris?\", # instruction\n",
+        "        \"\", # input\n",
+        "        \"\", # output - leave this blank for generation!\n",
+        "    )\n",
+        "], return_tensors = \"pt\").to(\"cuda\")\n",
+        "\n",
+        "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
+        "tokenizer.batch_decode(outputs)"
+      ],
+      "metadata": {
+        "id": "MKX_XKs_BNZR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**."
+      ],
+      "metadata": {
+        "id": "QQMjaNrjsU5_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if False:\n",
+        "    # I highly do NOT suggest - use Unsloth if possible\n",
+        "    from peft import AutoPeftModelForCausalLM\n",
+        "    from transformers import AutoTokenizer\n",
+        "    model = AutoPeftModelForCausalLM.from_pretrained(\n",
+        "        \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
+        "        load_in_4bit = load_in_4bit,\n",
+        "    )\n",
+        "    tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
+      ],
+      "metadata": {
+        "id": "yFfaXG0WsQuE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Saving to float16 for VLLM\n",
+        "\n",
+        "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. 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."
+      ],
+      "metadata": {
+        "id": "f422JgM9sdVT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Merge to 16bit\n",
+        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
+        "if True: model.push_to_hub_merged(\"Arun1982/LLama3-LoRA\", tokenizer, save_method = \"merged_16bit\", token = \"\"\"\")\n",
+        "\n",
+        "# Merge to 4bit\n",
+        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
+        "if True: model.push_to_hub_merged(\"Arun1982/LLama3-LoRA\", tokenizer, save_method = \"merged_4bit_forced\", token = \"\"\"\")\n",
+        "\n",
+        "# Just LoRA adapters\n",
+        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n",
+        "if True: model.push_to_hub_merged(\"Arun1982/LLama3-LoRA\", tokenizer, save_method = \"lora\", token = \"\"\"\")"
+      ],
+      "metadata": {
+        "id": "iHjt_SMYsd3P"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### GGUF / llama.cpp Conversion\n",
+        "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
+        "\n",
+        "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
+        "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
+        "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
+        "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K."
+      ],
+      "metadata": {
+        "id": "TCv4vXHd61i7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Save to 8bit Q8_0\n",
+        "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
+        "if True: model.push_to_hub_gguf(\"Arun1982/LLama3-LoRA\", tokenizer, token = \"\"\"\")\n",
+        "\n",
+        "# Save to 16bit GGUF\n",
+        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
+        "if True: model.push_to_hub_gguf(\"Arun1982/LLama3-LoRA\", tokenizer, quantization_method = \"f16\", token = \"\"\"\")\n",
+        "\n",
+        "# Save to q4_k_m GGUF\n",
+        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
+        "if True: model.push_to_hub_gguf(\"Arun1982/LLama3-LoRA\", tokenizer, quantization_method = \"q4_k_m\", token = \"\"\"\")"
+      ],
+      "metadata": {
+        "id": "FqfebeAdT073"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html)."
+      ],
+      "metadata": {
+        "id": "bDp0zNpwe6U_"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "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. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n",
+        "2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n",
+        "3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n",
+        "4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n",
+        "5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n",
+        "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n",
+        "7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n",
+        "8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n",
+        "\n",
+        "<div class=\"align-center\">\n",
+        "  <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
+        "  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
+        "  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Support our work if you can! Thanks!\n",
+        "</div>"
+      ],
+      "metadata": {
+        "id": "Zt9CHJqO6p30"
+      }
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}