metadata

license: gemma
datasets:
  - Mielikki/Erebus-87k
  - allura-org/r_shortstories_24k
base_model: allura-org/G2-9B-Sugarquill-v0
pipeline_tag: text-generation
library_name: transformers
tags:
  - llama-cpp
  - gguf-my-repo

Triangle104/G2-9B-Sugarquill-v0-Q8_0-GGUF

This model was converted to GGUF format from allura-org/G2-9B-Sugarquill-v0 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Model details:

An experimental continued pretrain of Gemma-2-9B-It-SPPO-Iter3 on assorted short story data from the web. I was trying to diversify Gemma's prose, without completely destroying it's smarts. I think I half-succeeded? This model could have used another epoch of training, but even this is already more creative and descriptive than it's base model, w/o becoming too silly. Doesn't seem to have degraded much in terms of core abilities as well. Should be usable both for RP and raw completion storywriting. I originally planned to use this in a merge, but I feel like this model is interesting enough to be released on it's own as well.

Model was trained by Auri.

Dedicated to Cahvay, who wanted a Gemma finetune from me for months by now, and to La Rata, who loves storywriter models.

GGUFs by Prodeus: https://huggingface.co/allura-org/G2-9B-Sugarquill-v0-GGUF

Training notes

This model was trained for 2 epochs on 10k rows (~18.7M tokens), taken equally from Erebus-87k and r_shortstories_24k datasets. It was trained on 8xH100 SXM node for 30 minutes with rsLoRA. I got complete nonsense reported to my wandb during this run, and logging stopped altogether after step 13 for some reason. Seems to be directly related to Gemma, as my training setup worked flawlessly for Qwen. Thanks to Kearm for helping with setting up LF on that node and to Featherless for providing it for EVA-Qwen2.5 (and this model, unknowingly lol) training.

Format

Model responds to Gemma instruct formatting, exactly like it's base model.

user {user message} model {response}

Training config

See LLaMA-Factory config

Model

model_name_or_path: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3 #ref_model: # Reference model for RL (optional, for everything besides SimPO, which doesn't take it at all) #ref_model_quantization_bit: 8 # 8 or 4

Method

stage: pt # pt, sft, rm, ppo, kto, dpo (includes orpo and simpo) do_train: true finetuning_type: lora # full, freeze or lora lora_target: all #pref_beta: 0.1 #pref_loss: simpo # sigmoid (dpo), orpo, simpo, ipo, hinge

Reward model

#reward_model: RLHFlow/ArmoRM-Llama3-8B-v0.1 # or sfairXC/FsfairX-Gemma2-RM-v0.1 or nvidia/Llama-3.1-Nemotron-70B-Reward-HF #reward_model_type: full # full, lora, api #reward_model_adapters: # Path to RM LoRA adapter(s) if using a LoRA RM #reward_model_quantization_bit: 8 # 4 or 8

Freeze

#freeze_trainable_layers: # The number of trainable layers for freeze (partial-parameter) fine-tuning. Positive number means n last layers to train, negative - n first layers to train #freeze_trainable_modules: # Name(s) of trainable modules for freeze (partial-parameter) fine-tuning. Use commas to separate #freeze_extra_modules: # Name(s) of modules apart from hidden layers to be set as trainable. Use commas to separate

LoRA

#loraplus_lr_ratio: 8.0 #loraplus_lr_embedding: use_dora: false use_rslora: true lora_rank: 64 # 64 is optimal for most trains on instruct, if training on base - use rslora or dora lora_alpha: 32 lora_dropout: 0.05 #pissa_init: true #pissa_iter: 16 #pissa_convert: true

QLoRA

quantization_bit: 8 # 2,3,4,5,6,8 in HQQ, 4 or 8 in bnb quantization_method: hqq # bitsandbytes or hqq

DeepSpeed

deepspeed: examples/deepspeed/ds_z2_config.json # ds_z3_config.json or ds_z2_config.json which is required for HQQ on multigpu

Dataset

dataset: sugarquill-10k # define in data/dataset_info.json cutoff_len: 8192 max_samples: 10000 overwrite_cache: true preprocessing_num_workers: 16 #template: chatml

Output

output_dir: saves/gemma/lora/sugarquill-1 logging_steps: 3 save_steps: 50 plot_loss: true compute_accuracy: true overwrite_output_dir: true

Train

per_device_train_batch_size: 1 # Effective b/s == per-device b/s * grad accum steps * number of GPUs gradient_accumulation_steps: 8 learning_rate: 3.0e-5 optim: paged_adamw_8bit # paged_adamw_8bit or adamw_torch usually num_train_epochs: 2.0 lr_scheduler_type: cosine # cosine, constant or linear warmup_ratio: 0.05 bf16: true ddp_timeout: 180000000 packing: true max_grad_norm: 1.0

Opts

flash_attn: fa2 # auto, disabled, sdpa, fa2 | Gemma will fallback to eager enable_liger_kernel: true # Pretty much must have if it works #use_unsloth: true # May not work with multigpu idk #use_adam_mini: true # Comment optim if using this

Eval

val_size: 0.1 per_device_eval_batch_size: 1 eval_strategy: steps eval_steps: 0.05

Misc

include_num_input_tokens_seen: true ddp_find_unused_parameters: false # Stupid thing tries to start distributed training otherwise upcast_layernorm: true

Inference for PPO

#max_new_tokens: 512 #temperature: 0.8 #top_k: 0 #top_p: 0.8

Tracking

report_to: wandb # or tensorboard or mlflow | LOGIN BEFORE STARTING TRAIN OR ELSE IT WILL CRASH run_name: G2-9B-Sugarquill-1

Merge Adapter

#export_dir: models/G2-9B-Sugarquill #export_size: 4 #export_device: gpu #export_legacy_format: false

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/G2-9B-Sugarquill-v0-Q8_0-GGUF --hf-file g2-9b-sugarquill-v0-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/G2-9B-Sugarquill-v0-Q8_0-GGUF --hf-file g2-9b-sugarquill-v0-q8_0.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/G2-9B-Sugarquill-v0-Q8_0-GGUF --hf-file g2-9b-sugarquill-v0-q8_0.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/G2-9B-Sugarquill-v0-Q8_0-GGUF --hf-file g2-9b-sugarquill-v0-q8_0.gguf -c 2048