README.md

---
license: apache-2.0
library_name: transformers
base_model: nbeerbower/mistral-nemo-gutenberg3-12B
datasets:
- jondurbin/gutenberg-dpo-v0.1
- nbeerbower/gutenberg2-dpo
- nbeerbower/gutenberg-moderne-dpo
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/mistral-nemo-gutenberg3-12B-Q6_K-GGUF
This model was converted to GGUF format from [`nbeerbower/mistral-nemo-gutenberg3-12B`](https://huggingface.co/nbeerbower/mistral-nemo-gutenberg3-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/nbeerbower/mistral-nemo-gutenberg3-12B) for more details on the model.

---
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,
)

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/mistral-nemo-gutenberg3-12B-Q6_K-GGUF --hf-file mistral-nemo-gutenberg3-12b-q6_k.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/mistral-nemo-gutenberg3-12B-Q6_K-GGUF --hf-file mistral-nemo-gutenberg3-12b-q6_k.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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-Q6_K-GGUF --hf-file mistral-nemo-gutenberg3-12b-q6_k.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/mistral-nemo-gutenberg3-12B-Q6_K-GGUF --hf-file mistral-nemo-gutenberg3-12b-q6_k.gguf -c 2048
```