--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft - code - lora - peft base_model: unsloth/tinyllama-chat-bnb-4bit pipeline_tag: text-generation datasets: Ramikan-BR/data-oss_instruct-decontaminated_python.jsonl --- # Uploaded model - **Developed by:** Ramikan-BR - **Model type:** [text-generation/Python Coder] - **Language(s) (NLP):** [en] - **License:** apache-2.0 - **Finetuned from model :** unsloth/tinyllama-chat-bnb-4bit ### Model Description ### Training Data datasets: [Ramikan-BR/data-oss_instruct-decontaminated_python.jsonl](https://huggingface.co/datasets/Ramikan-BR/data-oss_instruct-decontaminated_python.jsonl) ### Training Procedure The model was refined using [Unsloath](https://github.com/unslothai/unsloth). The dataset [ise-uiuc/Magicoder-OSS-Instruct-75K](https://huggingface.co/datasets/ise-uiuc/Magicoder-OSS-Instruct-75K/blob/main/data-oss_instruct-decontaminated.jsonl) was adjusted, leaving only data on python and divided into 10 parts, each refinement occurred for 2 epochs, using adafactor optimizer or adamw_8bit (adafactor seems to deliver less loss). ### Model Sources [optional] base_model: [unsloth/tinyllama-chat-bnb-4bit](https://huggingface.co/unsloth/tinyllama-chat-bnb-4bit) model: [Ramikan-BR/tinyllama-coder-py-4bit-v10](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10) gguf_f16: [tinyllama-coder-py-4bit-v10-unsloth.F16.gguf](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10/blob/main/tinyllama-coder-py-4bit-v10-unsloth.F16.gguf) gguf_Q4_K_M: [tinyllama-coder-py-4bit-v10-unsloth.Q4_K_M.gguf](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10/blob/main/tinyllama-coder-py-4bit-v10-unsloth.Q4_K_M.gguf) gguf_Q8_0: [tinyllama-coder-py-4bit-v10-unsloth.Q8_0.gguf](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10/blob/main/tinyllama-coder-py-4bit-v10-unsloth.Q8_0.gguf) #### Training Hyperparameters Notebook [Unsloath](https://github.com/unslothai/unsloth) that I used for AI refinement: [TinyLlama](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) ```python %%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 trl peft accelerate bitsandbytes # xformers "xformers<0.0.26" import os from google.colab import drive drive.mount('/content/drive') from unsloth import FastLanguageModel import torch max_seq_length = 4096 # 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/llama-2-13b-bnb-4bit", "unsloth/codellama-34b-bnb-4bit", "unsloth/tinyllama-bnb-4bit", "unsloth/gemma-7b-bnb-4bit", # New Google 6 trillion tokens model 2.5x faster! "unsloth/gemma-2b-bnb-4bit", ] # More models at https://huggingface.co/unsloth model, tokenizer = FastLanguageModel.from_pretrained( model_name = "Ramikan-BR/tinyllama-coder-py-4bit_LORA-v9", # "unsloth/tinyllama" for 16bit loading 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 = 256, # 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 = 512, lora_dropout = 0, # Currently only supports dropout = 0 bias = "none", # Currently only supports bias = "none" use_gradient_checkpointing = True, # @@@ IF YOU GET OUT OF MEMORY - set to True @@@ random_state = 3407, use_rslora = False, # We support rank stabilized LoRA loftq_config = None, # And LoftQ ) alpaca_prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Input: {} ### Output: {}""" EOS_TOKEN = tokenizer.eos_token def formatting_prompts_func(examples): inputs = examples["problem"] outputs = examples["solution"] texts = [] for input, output in zip(inputs, outputs): # Must add EOS_TOKEN, otherwise your generation will go on forever! text = alpaca_prompt.format(input, output) + EOS_TOKEN texts.append(text) return { "text" : texts} pass from datasets import load_dataset dataset = load_dataset('json', data_files='/content/drive/MyDrive/data-oss_instruct-py-10.jsonl', split='train') dataset = dataset.map(formatting_prompts_func, batched=True) from trl import SFTTrainer from transformers import TrainingArguments from unsloth import is_bfloat16_supported from transformers.utils import logging logging.set_verbosity_info() trainer = SFTTrainer( model = model, tokenizer = tokenizer, train_dataset = dataset, dataset_text_field = "text", max_seq_length = max_seq_length, dataset_num_proc = 2, packing = True, # Packs short sequences together to save time! args = TrainingArguments( per_device_train_batch_size = 2, gradient_accumulation_steps = 256, warmup_ratio = 0.1, num_train_epochs = 2, learning_rate = 2e-4, fp16 = not torch.cuda.is_bf16_supported(), bf16 = torch.cuda.is_bf16_supported(), logging_steps = 1, optim = "adafactor", # adamw_torch ou adamw_torch_fused +10% velocidade ou adafactor ou adamw_8bit weight_decay = 0.1, lr_scheduler_type = "linear", seed = 3407, output_dir = "outputs", ), ) trainer_stats = trainer.train() model.save_pretrained("lora_model") # Local saving tokenizer.save_pretrained("lora_model") model.push_to_hub("Ramikan-BR/tinyllama-coder-py-4bit_LORA-v10", token = "hf_...") # Online saving tokenizer.push_to_hub("Ramikan-BR/tinyllama-coder-py-4bit_LORA-v10", token = "hf_...") # Online saving # Merge to 16bit model.save_pretrained_merged("model", tokenizer, save_method = "merged_16bit",) model.push_to_hub_merged("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, save_method = "merged_16bit", token = "hf_...") # Merge to 4bit if False: model.save_pretrained_merged("model", tokenizer, save_method = "merged_4bit",) if False: model.push_to_hub_merged("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, save_method = "merged_4bit", token = "hf_...") # Just LoRA adapters if False: model.save_pretrained_merged("model", tokenizer, save_method = "lora",) if False: model.push_to_hub_merged("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, save_method = "lora", token = "hf_...") # Save to 8bit Q8_0 model.save_pretrained_gguf("model", tokenizer,) model.push_to_hub_gguf("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, token = "hf_...") # Save to 16bit GGUF model.save_pretrained_gguf("model", tokenizer, quantization_method = "f16") model.push_to_hub_gguf("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, quantization_method = "f16", token = "hf_...") # Save to q4_k_m GGUF model.save_pretrained_gguf("model", tokenizer, quantization_method = "q4_k_m") model.push_to_hub_gguf("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, quantization_method = "q4_k_m", token = "hf_...") Loss for 5 epochs in the last training session of the last part of the dataset: ==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1 \\ /| Num examples = 407 | Num Epochs = 5 O^O/ \_/ \ Batch size per device = 2 | Gradient Accumulation steps = 256 \ / Total batch size = 512 | Total steps = 5 "-____-" Number of trainable parameters = 201,850,880 [5/5 29:36, Epoch 3/5] Step Training Loss 1 0.568000 2 0.145300 3 0.506100 4 0.331900 5 0.276100 Parameters: This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [](https://github.com/unslothai/unsloth)