RP Chat
Ratings reflect this model as run on Arli AI.
Gemma-4-31B-Confetti is a text generation model available on Arli AI. It offers 31B parameters, 262K token context, reasoning support, vision input. Recommended use: RP Chat. Run it through the OpenAI-compatible chat completions API with unlimited requests and zero-log privacy.
View on Hugging FaceBack to ModelsNo hourly usage data available
Input (24H)
0
Output (24H)
0
Input (7D)
0
Output (7D)
0
Typical Total Time
N/A
Typical TTFT
N/A
Typical Input TPS
N/A
Typical Output TPS
N/A
Context Size
262144
Quantization
r32 on FP8
Engine
vllm
Creation Method
Finetune
Model Class
Gemma-4-31B - LoRA
Chat Template
Gemma4
Reasoning
Yes
Vision
Yes
Parameters
31B
Added At
10/31/2026
Gemma-4-31B-Confetti is available through an OpenAI-compatible chat completions endpoint. Point any OpenAI SDK at https://api.arliai.com/v1, pass Gemma-4-31B-Confetti as the model, and requests are served without logging.
cURL
curl https://api.arliai.com/v1/chat/completions \
-H "Authorization: Bearer $ARLIAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "Gemma-4-31B-Confetti",
"messages": [{"role": "user", "content": "Hello!"}]
}'Python
from openai import OpenAI
client = OpenAI(
base_url="https://api.arliai.com/v1",
api_key="YOUR_ARLIAI_API_KEY",
)
response = client.chat.completions.create(
model="Gemma-4-31B-Confetti",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)See the text generation docs for every parameter, or read the quick start guide to make your first request.
library_name: peft model_name: gemma4-31b-pt-embed-r32a8-textcomp tags:
This model was fine-tuned using SFT.
W&B run: https://wandb.ai/cooawoo-personal/Gemma4-31B/runs/lkdzz1dj
| Parameter | Value |
|---|---|
| Learning rate | 1e-05 |
| LR scheduler | rex (custom; max_lr=1e-5, min_lr=1e-6, warmup_ratio=0.05) |
| Per-device batch size | 1 |
| Gradient accumulation | 4 |
| Effective batch size | 4 |
| Epochs | 2 |
| Max sequence length | 4096 |
| Optimizer | OptimizerNames.PAGED_ADAMW_8BIT |
| Warmup ratio | 0.05 |
| Max gradient norm | 1.0 |
| Precision | bf16 |
| Loss type | nll |
| Chunked cross-entropy | yes |
| Parameter | Value |
|---|---|
| Rank (r) | 32 |
| Alpha | 8 |
| Target modules | .*language_model.layers.\d+.(self_attn.(q |
| rsLoRA | yes |
| Quantization | 4-bit (nf4) |
| Dataset | Samples | Total tokens | Trainable tokens |
|---|---|---|---|
| brainrot_chatlog.jsonl | 3,232 | 1,115,996 | 1,115,996 |
| erotica_quality_cleaned_20pct_seed42.json | 575 | 2,212,468 | 2,212,468 |
| worm_chapters.json | 658 | 2,268,255 | 2,268,255 |
| counter_signal_training_balanced.jsonl | 1,038 | 4,173,666 | 4,173,666 |
| Total | 5,503 | 9,770,385 | 9,770,385 |
model_name_or_path: Columbidae/gemma4-31b-pt-embed-it
data_config: data.yaml
prepared_dataset: prepared_packed
output_dir: gemma4-31b-pt-embed-r32a8-textcomp
attn_implementation: flex_attention
bf16: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
use_cce: true
chunked_mlp: true
chunked_mlp_chunks: 16
dataloader_num_workers: 2
dataloader_pin_memory: true
model_parallel: true
max_memory:
0: 16GiB
1: 24GiB
max_length: 4096
per_device_train_batch_size: 1
gradient_accumulation_steps: 4
pad_to_multiple_of: 128
use_peft: true
load_in_4bit: true
bnb_4bit_quant_type: nf4
lora_r: 32
lora_alpha: 8
lora_dropout: 0.0
use_rslora: true
lora_target_modules: .*language_model\.layers\.\d+\.(self_attn\.(q|k|v|o)_proj|mlp\.(gate|up|down)_proj)$
learning_rate: 1.0e-05
lr_scheduler_type: rex
warmup_ratio: 0.05
weight_decay: 0.0
max_grad_norm: 1.0
optim: paged_adamw_8bit
num_train_epochs: 2
saves_per_epoch: 2
save_total_limit: 4
rolling_save_steps: 30
rolling_save_total_limit: 1
logging_steps: 1
disable_tqdm: false
report_to: wandb
run_name: g4-31b-pt-embed-r32a8-textcomp
datasets:
- path: erotica_quality_cleaned_20pct_seed42.json
type: text
truncation_strategy: split
- path: counter_signal_training_balanced.jsonl
type: text
truncation_strategy: split
- path: worm_chapters.json
type: text
truncation_strategy: split
- path: brainrot_chatlog.jsonl
type: text
truncation_strategy: split
shuffle_datasets: true
shuffle_combined: true
shuffle_seed: 42
eval_split: 0
split_seed: 42
assistant_only_loss: false