--- language: - en - de - fr - it - pt - hi - es - th - zh - ko - ja license: llama3.2 pipeline_tag: text-generation tags: - roleplay - llama3 - sillytavern - idol - facebook - meta - pytorch - llama - llama-3 extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other --- # Llama-3.2-3B-Instruct-lyrics ![image/png](https://huggingface.co/aifeifei798/Llama-3.2-3B-Instruct-lyrics/resolve/main/Llama-3.2-3B-Instruct-lyrics.jpg) ## Model Information - write song on my phone ## Model Song (SUNO) - https://suno.com/@aifeifei ## virtual idol Twitter - https://x.com/aifeifei799 ## Datasets credits: - Koyd111/alpaca-hiphop-lyrics ## Prompt: write a song. song name: Style of Music(five components: genre, instrument, mood, gender, and timbre. ): [Intro] [Verse 1] [Chorus] [Verse 2] [Chorus] [Bridge] [Chorus] [Outro] ## Program: - unsloth ### Test Prograpm ```python from unsloth import FastLanguageModel from unsloth import PatchFastRL PatchFastRL("GRPO", 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. 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: {}""" model, tokenizer = FastLanguageModel.from_pretrained( model_name = "./Llama-3.2-3B-Instruct-lyrics", # or choose "unsloth/Llama-3.2-1B-Instruct" max_seq_length = max_seq_length, dtype = dtype, # load_in_4bit = load_in_4bit, # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf ) # alpaca_prompt = You MUST copy from above! inputs = tokenizer( [ alpaca_prompt.format( "", # instruction """ new write a pop love song. song name: Style of Music(five components: genre, instrument, mood, gender, and timbre. ): [Intro] [Verse 1] [Chorus] [Verse 2] [Chorus] [Bridge] [Chorus] [Outro] """, # 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 = 1024) ``` ### Trainer Prograpm ```python 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/Meta-Llama-3.1-8B-bnb-4bit", # Llama-3.1 2x faster "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit", "unsloth/Meta-Llama-3.1-70B-bnb-4bit", "unsloth/Meta-Llama-3.1-405B-bnb-4bit", # 4bit for 405b! "unsloth/Mistral-Small-Instruct-2409", # Mistral 22b 2x faster! "unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "unsloth/Phi-3.5-mini-instruct", # Phi-3.5 2x faster! "unsloth/Phi-3-medium-4k-instruct", "unsloth/gemma-2-9b-bnb-4bit", "unsloth/gemma-2-27b-bnb-4bit", # Gemma 2x faster! "unsloth/Llama-3.2-1B-bnb-4bit", # NEW! Llama 3.2 models "unsloth/Llama-3.2-1B-Instruct-bnb-4bit", "unsloth/Llama-3.2-3B-bnb-4bit", "unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "unsloth/Llama-3.3-70B-Instruct-bnb-4bit" # NEW! Llama 3.3 70B! ] # More models at https://huggingface.co/unsloth model, tokenizer = FastLanguageModel.from_pretrained( model_name = "./Llama-3.2-3B-Instruct-unsloth-bnb-4bit", # or choose "unsloth/Llama-3.2-1B-Instruct" max_seq_length = max_seq_length, dtype = dtype, load_in_4bit = load_in_4bit, # token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf ) 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 ) from unsloth.chat_templates import get_chat_template tokenizer = get_chat_template( tokenizer, chat_template = "llama-3.2", ) 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("Koyd111/alpaca-hiphop-lyrics", split = "train") dataset = dataset.map(formatting_prompts_func, batched = True,) print(dataset[5]) from trl import SFTTrainer from transformers import TrainingArguments, DataCollatorForSeq2Seq from unsloth import is_bfloat16_supported trainer = SFTTrainer( model = model, tokenizer = tokenizer, train_dataset = dataset, dataset_text_field = "text", max_seq_length = max_seq_length, dataset_num_proc = 16, 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, # num_train_epochs = 1, # Set this for 1 full training run. max_steps = 60, learning_rate = 2e-4, fp16 = not is_bfloat16_supported(), bf16 = is_bfloat16_supported(), logging_steps = 1, optim = "adamw_8bit", weight_decay = 0.01, lr_scheduler_type = "linear", seed = 3407, output_dir = "outputs", report_to = "none", # Use this for WandB etc ), ) trainer_stats = trainer.train() # 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") # 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) outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True) tokenizer.batch_decode(outputs) model.save_pretrained("Llama-3.2-3B-Instruct-lyrics-lora") # Local saving tokenizer.save_pretrained("Llama-3.2-3B-Instruct-lyrics-lora") model.save_pretrained_merged("Llama-3.2-3B-Instruct-lyrics", tokenizer) ``` ## Questions - This model is solely for learning and testing purposes, and errors in output are inevitable. We do not take responsibility for the output results. If the output content is to be used, it must be modified; if not modified, we will assume it has been altered. - For commercial licensing, please refer to the Llama 3.2 agreement. # Llama-3.2-3B-Instruct-unsloth-bnb-4bit Information

See our collection for versions of Llama 3.2 including GGUF & 4-bit formats.

Unsloth's Dynamic 4-bit Quants is selectively quantized, greatly improving accuracy over standard 4-bit.

Fine-tune LLMs 2-5x faster with 70% less memory via Unsloth!

We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb # unsloth/Llama-3.2-3B-unsloth-bnb-4bit For more details on the model, please go to Meta's original [model card](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) ## ✨ Finetune for Free 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. | Unsloth supports | Free Notebooks | Performance | Memory use | |-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------| | **Llama-3.2 (3B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) | 2.4x faster | 58% less | | **Llama-3.2 (11B vision)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) | 2x faster | 60% less | | **Qwen2 VL (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_VL_(7B)-Vision.ipynb) | 1.8x faster | 60% less | | **Qwen2.5 (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) | 2x faster | 60% less | | **Llama-3.1 (8B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb) | 2.4x faster | 58% less | | **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3.5_Mini-Conversational.ipynb) | 2x faster | 50% less | | **Gemma 2 (9B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_(9B)-Alpaca.ipynb) | 2.4x faster | 58% less | | **Mistral (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb) | 2.2x faster | 62% less | [](https://docs.unsloth.ai) - This [Llama 3.2 conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) is useful for ShareGPT ChatML / Vicuna templates. - This [text completion notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_(7B)-Text_Completion.ipynb) 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. ## Special Thanks A huge thank you to the Meta and Llama team for creating and releasing these models. ## Model Information The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. **Model developer**: Meta **Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. **Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. **Llama 3.2 family of models** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date:** Sept 25, 2024 **Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. **License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).