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	---
	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
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	## 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
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	## PLEASE INSTEAD USE TENSORDOCK ITS CHEAPER AND DOESNT DISCONNECT YOU
	tensordock.com
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	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 = "")
	```