nbeerbower
/

llama-3-gutenberg-8B

@@ -1,199 +1,120 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+base_model:
+  - nbeerbower/llama-3-bophades-v3-8B
+datasets:
+- jondurbin/gutenberg-dpo-v0.1
+license: other
+license_name: llama3
 ---
+# llama-3-dragonmaid-8B
+This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
+[nbeerbower/llama-3-bophades-v3-8B](https://huggingface.co/nbeerbower/llama-3-bophades-v3-8B) finetuned on [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1).
+### Method
+Finetuned using an A100 on Google Colab.
+[Fine-Tune Your Own Llama 2 Model in a Colab Notebook](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html)
+### Configuration
+Dataset preparation, system prompt:
+```python
+def chatml_format(example):
+    # Format instruction
+    prompt = "<|im_start|>user\n" + example['prompt'] + "<|im_end|>\n<|im_start|>assistant\n"
+    # Format chosen answer
+    chosen = example['chosen'] + "<|im_end|>\n"
+    # Format rejected answer
+    rejected = example['rejected'] + "<|im_end|>\n"
+    return {
+        "prompt": prompt,
+        "chosen": chosen,
+        "rejected": rejected,
+    }
+dataset = load_dataset("jondurbin/gutenberg-dpo-v0.1")['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:
+```python
+# 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=2,
+    gradient_accumulation_steps=2,
+    gradient_checkpointing=True,
+    learning_rate=2e-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
+)
+```