README.md

metadata

library_name: transformers
base_model:
  - nbeerbower/llama-3-bophades-v3-8B
datasets:
  - tasksource/ScienceQA_text_only
license: other
license_name: llama3

llama-3-wissenschaft-8B-v2

This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT

nbeerbower/llama-3-bophades-v3-8B finetuned on tasksource/ScienceQA_text_only.

Method

Finetuned using an A100 on Google Colab.

Fine-Tune Your Own Llama 2 Model in a Colab Notebook

Configuration

Dataset preparation, system prompt:

def get_correct_answer(example):
    answerIdx = example['answer']
    choices = example['choices']
    return choices[answerIdx]

def get_wrong_answer(example):
    choices = example['choices']
    answerIdx = example['answer']
    for i in range(len(choices)):
        if i != answerIdx:
            return choices[i]

def chatml_format(example):
    # Format system
    systemMessage = "Read the following lecture, then answer the question."
    system = "<|im_start|>system\n" + systemMessage + "<|im_end|>\n"

    # Format instruction
    instruction = ""
    if example.get('lecture'):
        instruction = "Lecture: " + example['lecture'] + "\nQuestion: "
    else:
        instruction = "Question: "
    instruction += example['question']

    # Format prompt
    prompt = "<|im_start|>user\n" + instruction + "<|im_end|>\n<|im_start|>assistant\n"

    # Format chosen answer
    chosen = get_correct_answer(example) + "<|im_end|>\n"

    # Format rejected answer
    rejected = get_wrong_answer(example) + "<|im_end|>\n"

    return {
        "prompt": system + prompt,
        "chosen": chosen,
        "rejected": rejected,
    }

dataset = load_dataset("tasksource/ScienceQA_text_only")['train']

# Save columns
original_columns = dataset.column_names

# Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"

# Format dataset
dataset = dataset.map(
    chatml_format,
    remove_columns=original_columns
)

LoRA, model, and training settings:

# LoRA configuration
peft_config = LoraConfig(
    r=16,
    lora_alpha=16,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
)

# Model to fine-tune
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    load_in_4bit=True
)
model.config.use_cache = False

# Reference model
ref_model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    load_in_4bit=True
)

# Training arguments
training_args = TrainingArguments(
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    gradient_checkpointing=True,
    learning_rate=5e-5,
    lr_scheduler_type="cosine",
    max_steps=1000,
    save_strategy="no",
    logging_steps=1,
    output_dir=new_model,
    optim="paged_adamw_32bit",
    warmup_steps=100,
    bf16=True,
    report_to="wandb",
)

# Create DPO trainer
dpo_trainer = DPOTrainer(
    model,
    ref_model,
    args=training_args,
    train_dataset=dataset,
    tokenizer=tokenizer,
    peft_config=peft_config,
    beta=0.1,
    max_prompt_length=1024,
    max_length=1536,
    force_use_ref_model=True
)