Triangle104/mistral-nemo-gutenberg3-12B-Q4_K_S-GGUF

This model was converted to GGUF format from nbeerbower/mistral-nemo-gutenberg3-12B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

Mahou-1.5-mistral-nemo-12B-lorablated finetuned on jondurbin/gutenberg-dpo-v0.1, nbeerbower/gutenberg2-dpo, and nbeerbower/gutenberg-moderne-dpo. Method

ORPO tuned 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=4096, max_prompt_length=2048, max_completion_length=2048, beta=0.1, per_device_train_batch_size=2, per_device_eval_batch_size=2, 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, )

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/mistral-nemo-gutenberg3-12B-Q4_K_S-GGUF --hf-file mistral-nemo-gutenberg3-12b-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/mistral-nemo-gutenberg3-12B-Q4_K_S-GGUF --hf-file mistral-nemo-gutenberg3-12b-q4_k_s.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/mistral-nemo-gutenberg3-12B-Q4_K_S-GGUF --hf-file mistral-nemo-gutenberg3-12b-q4_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/mistral-nemo-gutenberg3-12B-Q4_K_S-GGUF --hf-file mistral-nemo-gutenberg3-12b-q4_k_s.gguf -c 2048