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copy_of_alpaca_+_llama_3_8b_full_example.py

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+# -*- coding: utf-8 -*-
+"""Copy of Alpaca + Llama-3 8b full example.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/12GTGPtaZvutZlE2GUHmeXVrvq2dgJdu6
+
+## To run this, press "*Runtime*" and press "*Run all*" on a **free** Tesla T4 Google Colab instance!
+
+To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).
+
+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).
+
+**Llama-3 8b is trained on a crazy 15 trillion tokens! Llama-2 was 2 trillion.**
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%capture
+# # Installs Unsloth, Xformers (Flash Attention) and all other packages!
+# !pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
+# !pip install --no-deps "xformers<0.0.26" trl peft accelerate bitsandbytes
+
+token = "" #HF Token
+
+"""* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc
+* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.
+* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.
+* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.
+* [**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.
+"""
+
+from unsloth import FastLanguageModel
+import torch
+max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
+dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+
+# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
+fourbit_models = [
+    "unsloth/mistral-7b-bnb-4bit",
+    "unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
+    "unsloth/llama-2-7b-bnb-4bit",
+    "unsloth/gemma-7b-bnb-4bit",
+    "unsloth/gemma-7b-it-bnb-4bit", # Instruct version of Gemma 7b
+    "unsloth/gemma-2b-bnb-4bit",
+    "unsloth/gemma-2b-it-bnb-4bit", # Instruct version of Gemma 2b
+    "unsloth/llama-3-8b-bnb-4bit", # [NEW] 15 Trillion token Llama-3
+] # More models at https://huggingface.co/unsloth
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/llama-3-8b-bnb-4bit",
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+    # token = token, # use one if using gated models like meta-llama/Llama-2-7b-hf
+)
+
+"""We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"""
+
+model = FastLanguageModel.get_peft_model(
+    model,
+    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
+    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
+    # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
+    use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
+    random_state = 3407,
+    use_rslora = False,  # We support rank stabilized LoRA
+    loftq_config = None, # And LoftQ
+)
+
+"""<a name="Data"></a>
+### Data Prep
+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.
+
+**[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).
+
+**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!
+
+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).
+
+For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing).
+"""
+
+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.
+
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}"""
+
+EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
+def formatting_prompts_func(examples):
+    instructions = examples["instruction"]
+    inputs       = examples["input"]
+    outputs      = examples["output"]
+    texts = []
+    for instruction, input, output in zip(instructions, inputs, outputs):
+        # Must add EOS_TOKEN, otherwise your generation will go on forever!
+        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN
+        texts.append(text)
+    return { "text" : texts, }
+pass
+
+from datasets import load_dataset
+dataset = load_dataset("yahma/alpaca-cleaned", split = "train")
+dataset = dataset.map(formatting_prompts_func, batched = True,)
+
+"""<a name="Train"></a>
+### Train the model
+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`!
+"""
+
+from trl import SFTTrainer
+from transformers import TrainingArguments
+
+trainer = SFTTrainer(
+    model = model,
+    tokenizer = tokenizer,
+    train_dataset = dataset,
+    dataset_text_field = "text",
+    max_seq_length = max_seq_length,
+    dataset_num_proc = 2,
+    packing = False, # Can make training 5x faster for short sequences.
+    args = TrainingArguments(
+        per_device_train_batch_size = 2,
+        gradient_accumulation_steps = 4,
+        warmup_steps = 5,
+        max_steps = 60,
+        learning_rate = 2e-4,
+        fp16 = not torch.cuda.is_bf16_supported(),
+        bf16 = torch.cuda.is_bf16_supported(),
+        logging_steps = 1,
+        optim = "adamw_8bit",
+        weight_decay = 0.01,
+        lr_scheduler_type = "linear",
+        seed = 3407,
+        output_dir = "outputs",
+    ),
+)
+
+#@title Show current memory stats
+gpu_stats = torch.cuda.get_device_properties(0)
+start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
+max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
+print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
+print(f"{start_gpu_memory} GB of memory reserved.")
+
+trainer_stats = trainer.train()
+
+#@title Show final memory and time stats
+used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
+used_memory_for_lora = round(used_memory - start_gpu_memory, 3)
+used_percentage = round(used_memory         /max_memory*100, 3)
+lora_percentage = round(used_memory_for_lora/max_memory*100, 3)
+print(f"{trainer_stats.metrics['train_runtime']} seconds used for training.")
+print(f"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.")
+print(f"Peak reserved memory = {used_memory} GB.")
+print(f"Peak reserved memory for training = {used_memory_for_lora} GB.")
+print(f"Peak reserved memory % of max memory = {used_percentage} %.")
+print(f"Peak reserved memory for training % of max memory = {lora_percentage} %.")
+
+"""<a name="Inference"></a>
+### Inference
+Let's run the model! You can change the instruction and input - leave the output blank!
+"""
+
+# alpaca_prompt = Copied from above
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+        "Continue the fibonnaci sequence.", # instruction
+        "1, 1, 2, 3, 5, 8", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
+tokenizer.batch_decode(outputs)
+
+""" You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!"""
+
+# alpaca_prompt = Copied from above
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+        "Continue the fibonnaci sequence.", # instruction
+        "1, 1, 2, 3, 5, 8", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)
+
+"""<a name="Save"></a>
+### Saving, loading finetuned models
+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.
+
+**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!
+"""
+
+#model.save_pretrained("lora_model") # Local saving
+#tokenizer.save_pretrained("lora_model")
+model.push_to_hub("ArunKr/LLama3-LoRA", token = token) # Online saving
+tokenizer.push_to_hub("ArunKr/LLama3-LoRA", token = token) # Online saving
+
+"""Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"""
+
+if False:
+    from unsloth import FastLanguageModel
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name = "lora_model", # YOUR MODEL YOU USED FOR TRAINING
+        max_seq_length = max_seq_length,
+        dtype = dtype,
+        load_in_4bit = load_in_4bit,
+    )
+    FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+
+# alpaca_prompt = You MUST copy from above!
+
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+        "What is a famous tall tower in Paris?", # instruction
+        "", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
+tokenizer.batch_decode(outputs)
+
+"""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**."""
+
+if False:
+    # I highly do NOT suggest - use Unsloth if possible
+    from peft import AutoPeftModelForCausalLM
+    from transformers import AutoTokenizer
+    model = AutoPeftModelForCausalLM.from_pretrained(
+        "lora_model", # YOUR MODEL YOU USED FOR TRAINING
+        load_in_4bit = load_in_4bit,
+    )
+    tokenizer = AutoTokenizer.from_pretrained("lora_model")
+
+"""### Saving to float16 for VLLM
+
+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.
+"""
+
+# Merge to 16bit
+if False: model.save_pretrained_merged("model", tokenizer, save_method = "merged_16bit",)
+if True: model.push_to_hub_merged("ArunKr/LLama3-LoRA", tokenizer, save_method = "merged_16bit", token = token)
+
+# Merge to 4bit
+if False: model.save_pretrained_merged("model", tokenizer, save_method = "merged_4bit",)
+if True: model.push_to_hub_merged("ArunKr/LLama3-LoRA", tokenizer, save_method = "merged_4bit_forced", token = token)
+
+# Just LoRA adapters
+if False: model.save_pretrained_merged("model", tokenizer, save_method = "lora",)
+if True: model.push_to_hub_merged("ArunKr/LLama3-LoRA", tokenizer, save_method = "lora", token = token)
+
+"""### GGUF / llama.cpp Conversion
+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.
+
+Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):
+* `q8_0` - Fast conversion. High resource use, but generally acceptable.
+* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.
+* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.
+"""
+
+# Save to 8bit Q8_0
+if False: model.save_pretrained_gguf("model", tokenizer,)
+if True: model.push_to_hub_gguf("ArunKr/LLama3-LoRA", tokenizer, token = token)
+
+# Save to 16bit GGUF
+if False: model.save_pretrained_gguf("model", tokenizer, quantization_method = "f16")
+if True: model.push_to_hub_gguf("ArunKr/LLama3-LoRA", tokenizer, quantization_method = "f16", token = token)
+
+# Save to q4_k_m GGUF
+if False: model.save_pretrained_gguf("model", tokenizer, quantization_method = "q4_k_m")
+if True: model.push_to_hub_gguf("ArunKr/LLama3-LoRA", tokenizer, quantization_method = "q4_k_m", token = token)
+
+"""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).
+
+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!
+
+Some other links:
+1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)
+2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)
+3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)
+4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)
+5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)
+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)!
+7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)
+8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)
+"""
+