README.md

---
license: apache-2.0
library_name: transformers
base_model:
- nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated
datasets:
- nbeerbower/Schule-DPO
- nbeerbower/Arkhaios-DPO
- nbeerbower/Purpura-DPO
---

![image/png](https://huggingface.co./nbeerbower/mistral-nemo-kartoffel-12B/resolve/main/kartoffel.png?download=true)

# mistral-nemo-kartoffel-12B

[Mahou-1.5-mistral-nemo-12B-lorablated](https://huggingface.co./nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated) finetuned on various datasets.

### Method

[ORPO tuned](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) with 8x A100 for 2 epochs.

QLoRA config:
```
# QLoRA config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch_dtype,
    bnb_4bit_use_double_quant=True,
)

# LoRA config
peft_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules=['up_proj', 'down_proj', 'gate_proj', 'k_proj', 'q_proj', 'v_proj', 'o_proj']
)
```

Training config:
```
orpo_args = ORPOConfig(
    run_name=new_model,
    learning_rate=8e-6,
    lr_scheduler_type="linear",
    max_length=2048,
    max_prompt_length=1024,
    max_completion_length=1024,
    beta=0.1,
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    gradient_accumulation_steps=1,
    optim="paged_adamw_8bit",
    num_train_epochs=2,
    evaluation_strategy="steps",
    eval_steps=0.2,
    logging_steps=1,
    warmup_steps=10,
    max_grad_norm=10,
    report_to="wandb",
    output_dir="./results/",
    bf16=True,
    gradient_checkpointing=True,
)
```