Update README.md

README.md

@@ -10,6 +10,7 @@ tags:
 
 ---
 
+
 # Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
 
 A reupload from https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
@@ -18,76 +19,22 @@ We have a Google Colab Tesla T4 notebook for TinyLlama with 4096 max sequence le
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk)
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth)
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="400"/>](https://github.com/unslothai/unsloth)
-
-```python
-from unsloth import FastLanguageModel
-import torch
-from trl import SFTTrainer
-from transformers import TrainingArguments
-from datasets import load_dataset
-max_seq_length = 2048 # Supports RoPE Scaling interally, so choose any!
-# Get LAION dataset
-url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl"
-dataset = load_dataset("json", data_files = {"train" : url}, split = "train")
-
-# 4bit pre quantized models we support - 4x faster downloading!
-fourbit_models = [
-    "unsloth/mistral-7b-bnb-4bit",
-    "unsloth/llama-2-7b-bnb-4bit",
-    "unsloth/llama-2-13b-bnb-4bit",
-    "unsloth/codellama-34b-bnb-4bit",
-    "unsloth/tinyllama-bnb-4bit",
-] # Go to https://huggingface.co/unsloth for more 4-bit models!
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 
-# Load Llama model
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "unsloth/mistral-7b-bnb-4bit", # Supports Llama, Mistral - replace this!
-    max_seq_length = max_seq_length,
-    dtype = None,
-    load_in_4bit = True,
-)
+## ✨ Finetune for Free
 
-# Do model patching and add fast LoRA weights
-model = FastLanguageModel.get_peft_model(
-    model,
-    r = 16,
-    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
-                      "gate_proj", "up_proj", "down_proj",],
-    lora_alpha = 16,
-    lora_dropout = 0, # Supports any, but = 0 is optimized
-    bias = "none",    # Supports any, but = "none" is optimized
-    use_gradient_checkpointing = True,
-    random_state = 3407,
-    max_seq_length = max_seq_length,
-    use_rslora = False,  # We support rank stabilized LoRA
-    loftq_config = None, # And LoftQ
-)
+All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
 
-trainer = SFTTrainer(
-    model = model,
-    train_dataset = dataset,
-    dataset_text_field = "text",
-    max_seq_length = max_seq_length,
-    tokenizer = tokenizer,
-    args = TrainingArguments(
-        per_device_train_batch_size = 2,
-        gradient_accumulation_steps = 4,
-        warmup_steps = 10,
-        max_steps = 60,
-        fp16 = not torch.cuda.is_bf16_supported(),
-        bf16 = torch.cuda.is_bf16_supported(),
-        logging_steps = 1,
-        output_dir = "outputs",
-        optim = "adamw_8bit",
-        seed = 3407,
-    ),
-)
-trainer.train()
+| Unsloth supports          |    Free Notebooks                                                                                           | Performance | Memory use |
+|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
+| **Gemma 7b**      | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)               | 2.4x faster | 58% less |
+| **Mistral 7b**    | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)               | 2.2x faster | 62% less |
+| **Llama-2 7b**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)               | 2.2x faster | 43% less |
+| **TinyLlama**  | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)              | 3.9x faster | 74% less |
+| **CodeLlama 34b** A100   | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)              | 1.9x faster | 27% less |
+| **Mistral 7b** 1xT4  | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less |
+| **DPO - Zephyr**     | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)               | 1.9x faster | 19% less |
 
-# Go to https://github.com/unslothai/unsloth/wiki for advanced tips like
-# (1) Saving to GGUF / merging to 16bit for vLLM
-# (2) Continued training from a saved LoRA adapter
-# (3) Adding an evaluation loop / OOMs
-# (4) Cutomized chat templates
-```
+- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
+- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
+- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.