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README.md
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This model was converted to GGUF format from [`nbeerbower/mistral-nemo-kartoffel-12B`](https://huggingface.co/nbeerbower/mistral-nemo-kartoffel-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/nbeerbower/mistral-nemo-kartoffel-12B) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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This model was converted to GGUF format from [`nbeerbower/mistral-nemo-kartoffel-12B`](https://huggingface.co/nbeerbower/mistral-nemo-kartoffel-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/nbeerbower/mistral-nemo-kartoffel-12B) for more details on the model.
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---
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Model details:
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-
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Mahou-1.5-mistral-nemo-12B-lorablated finetuned on various datasets.
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Method
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ORPO tuned with 8x A100 for 2 epochs.
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QLoRA config:
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# QLoRA config
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch_dtype,
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bnb_4bit_use_double_quant=True,
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)
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# LoRA config
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peft_config = LoraConfig(
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r=16,
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lora_alpha=32,
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lora_dropout=0.05,
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bias="none",
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task_type="CAUSAL_LM",
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target_modules=['up_proj', 'down_proj', 'gate_proj', 'k_proj', 'q_proj', 'v_proj', 'o_proj']
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)
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Training config:
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orpo_args = ORPOConfig(
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run_name=new_model,
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learning_rate=8e-6,
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lr_scheduler_type="linear",
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max_length=2048,
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max_prompt_length=1024,
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max_completion_length=1024,
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beta=0.1,
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per_device_train_batch_size=4,
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per_device_eval_batch_size=4,
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gradient_accumulation_steps=1,
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optim="paged_adamw_8bit",
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num_train_epochs=2,
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evaluation_strategy="steps",
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eval_steps=0.2,
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logging_steps=1,
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warmup_steps=10,
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max_grad_norm=10,
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report_to="wandb",
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output_dir="./results/",
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bf16=True,
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gradient_checkpointing=True,
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)
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---
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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