--- language: - en license: apache-2.0 model-index: - name: test_dataset_Codellama-3-8B results: - task: type: text-generation dataset: name: HumanEval type: openai_humaneval metrics: - type: pass@1 value: 0.630 name: pass@1 verified: false --- ## Please note this model is a test, the full finetuned version can be found here: https://huggingface.co/rombodawg/Llama-3-8B-Instruct-Coder _______________________________________________________ ## GOOGLE COLAB IS A SCAM DO NOT USE THE PAID VERSION ## THEY WILL DISCONNECT YOUR RUNTIME BEFORE EVEN 24 HOURS https://github.com/googlecolab/colabtools/issues/3451 _________________________________________________________________________________________ ## PLEASE INSTEAD USE TENSORDOCK ITS CHEAPER AND DOESNT DISCONNECT YOU tensordock.com _________________________________________________________________________________________ __________________________________________________________________________________________________________________________________________________________________________________ _________________________________________________________________________________________ _________________________________________________________________________________________ _________________________________________________________________________________________ _________________________________________________________________________________________ _________________________________________________________________________________________ _________________________________________________________________________________________ _________________________________________________________________________________________ _________________________________________________________________________________________ _________________________________________________________________________________________ _________________________________________________________________________________________ This is unsloth/llama-3-8b-Instruct trained on the Replete-AI/code-test-dataset using the code bellow with unsloth and google colab with under 15gb of vram. This training was complete in about 40 minutes total. __________________________________________________________________________ Colab doc if you dont want to copy the code by hand: - https://colab.research.google.com/drive/1bX4BsjLcdNJnoAf7lGXmWOgaY8yekg8p?usp=sharing __________________________________________________________________________ Copy from my announcement in my discord: ``` If anyone wants to train their own llama-3-8b model for free on any dataset that has around 1,500 lines of data or less you can now do it easily by using the code I provided in the model card for my test model in this repo and google colab. The training for this model uses (Unsloth + Qlora + Galore) to achieve the ability for training under such low vram. ``` For anyone that is new to coding and training Ai, all your really have to edit is 1. (max_seq_length = 8192) To match the max tokens of the dataset or model you are using 2. (model_name = "unsloth/llama-3-8b-Instruct",) Change what model you are finetuning, this setup is specifically for llama-3-8b 3. (alpaca_prompt =) Change the prompt format, this one is setup to meet llama-3-8b-instruct format, but match it to your specifications. 4. (dataset = load_dataset("Replete-AI/code-test-dataset", split = "train")) What dataset you are using from huggingface 5. (model.push_to_hub_merged("rombodawg/test_dataset_Codellama-3-8B", tokenizer, save_method = "merged_16bit", token = "")) 6. For the above you need to change "rombodawg" to your Hugginface name, "test_dataset_Codellama-3-8B" to the model name you want saved as, and in token = "" you need to put your huggingface write token so the model can be saved. ```Python %%capture import torch major_version, minor_version = torch.cuda.get_device_capability() # Must install separately since Colab has torch 2.2.1, which breaks packages !pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" if major_version >= 8: # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40) !pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes else: # Use this for older GPUs (V100, Tesla T4, RTX 20xx) !pip install --no-deps xformers trl peft accelerate bitsandbytes pass ``` ```Python !pip install galore_torch ``` ```Python from unsloth import FastLanguageModel import torch max_seq_length = 8192 # 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-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 ) ``` ```Python 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 ) ``` ```Python alpaca_prompt = """<|begin_of_text|><|start_header_id|>system<|end_header_id|> Below is an instruction that describes a task, Write a response that appropriately completes the request.<|eot_id|><|start_header_id|>user<|end_header_id|> {}<|eot_id|><|start_header_id|>assistant<|end_header_id|>{}""" EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN def formatting_prompts_func(examples): inputs = examples["human"] outputs = examples["assistant"] 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("Replete-AI/code-test-dataset", split = "train") dataset = dataset.map(formatting_prompts_func, batched = True,) ``` ```Python from trl import SFTTrainer from transformers import TrainingArguments from galore_torch import GaLoreAdamW8bit import torch.nn as nn galore_params = [] target_modules_list = ["attn", "mlp"] for module_name, module in model.named_modules(): if not isinstance(module, nn.Linear): continue if not any(target_key in module_name for target_key in target_modules_list): continue print('mod ', module_name) galore_params.append(module.weight) id_galore_params = [id(p) for p in galore_params] regular_params = [p for p in model.parameters() if id(p) not in id_galore_params] param_groups = [{'params': regular_params}, {'params': galore_params, 'rank': 64, 'update_proj_gap': 200, 'scale': 0.25, 'proj_type': 'std'}] optimizer = GaLoreAdamW8bit(param_groups, lr=2e-5) trainer = SFTTrainer( model = model, tokenizer = tokenizer, train_dataset = dataset, optimizers=(optimizer, None), dataset_text_field = "text", max_seq_length = max_seq_length, dataset_num_proc = 2, packing = True, # Can make training 5x faster for short sequences. args = TrainingArguments( per_device_train_batch_size = 1, gradient_accumulation_steps = 4, warmup_steps = 5, learning_rate = 2e-4, fp16 = not torch.cuda.is_bf16_supported(), bf16 = torch.cuda.is_bf16_supported(), logging_steps = 1, weight_decay = 0.01, lr_scheduler_type = "linear", seed = 3407, output_dir = "outputs", ), ) ``` ```Python trainer_stats = trainer.train() model.save_pretrained_merged("model", tokenizer, save_method = "merged_16bit",) model.push_to_hub_merged("rombodawg/test_dataset_Codellama-3-8B", tokenizer, save_method = "merged_16bit", token = "") ```