Instructions to use BirdToast/Gemma-4-31B-glimmer-rp-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use BirdToast/Gemma-4-31B-glimmer-rp-v0.1 with PEFT:
```
Task type is invalid.
```

How to use BirdToast/Gemma-4-31B-glimmer-rp-v0.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BirdToast/Gemma-4-31B-glimmer-rp-v0.1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("BirdToast/Gemma-4-31B-glimmer-rp-v0.1")
model = AutoModelForImageTextToText.from_pretrained("BirdToast/Gemma-4-31B-glimmer-rp-v0.1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BirdToast/Gemma-4-31B-glimmer-rp-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BirdToast/Gemma-4-31B-glimmer-rp-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BirdToast/Gemma-4-31B-glimmer-rp-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BirdToast/Gemma-4-31B-glimmer-rp-v0.1

SGLang

How to use BirdToast/Gemma-4-31B-glimmer-rp-v0.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BirdToast/Gemma-4-31B-glimmer-rp-v0.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BirdToast/Gemma-4-31B-glimmer-rp-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BirdToast/Gemma-4-31B-glimmer-rp-v0.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BirdToast/Gemma-4-31B-glimmer-rp-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BirdToast/Gemma-4-31B-glimmer-rp-v0.1 with Docker Model Runner:
```
docker model run hf.co/BirdToast/Gemma-4-31B-glimmer-rp-v0.1
```

gemma4-31b-it-glimmer-rp-r16a32

This model is a fine-tuned version of google/gemma-4-31B-it.

W&B run: https://wandb.ai/cooawoo-personal/Gemma4-31B/runs/nbmb3v4h

Training procedure

Hyperparameters

Parameter	Value
Learning rate	`1e-05`
LR scheduler	rex (custom; max_lr=1e-5, min_lr=1e-6, warmup_ratio=0.05)_WITH_WARMUP
Per-device batch size	1
Gradient accumulation	4
Effective batch size	4
Epochs	1
Max sequence length	6144
Optimizer	OptimizerNames.PAGED_ADAMW_8BIT
Warmup ratio	0.05
Max gradient norm	1.0
Precision	bf16
Loss type	nll
Assistant-only loss	yes
Chunked cross-entropy	yes

LoRA configuration

Parameter	Value
Rank (r)	16
Alpha	32
Target modules	.*language_model.layers.\d+.(self_attn.(q
Quantization	4-bit (nf4)

Dataset statistics

Dataset	Samples	Total tokens	Trainable tokens
writing_critique.jsonl	1,586	1,317,233	599,216
instruct.jsonl	962	933,867	838,340
marvin_style_bible.jsonl	2,549	11,096,548	10,492,459
rp_generation_mistral.jsonl	255	825,572	375,698
rp_analysis.jsonl	244	725,115	177,861
rp_generation_final.jsonl	129	513,974	240,019
Total	5,725	15,412,309	12,723,593

Training config

model_name_or_path: google/gemma-4-31B-it
data_config: data.yaml
prepared_dataset: prepared_packed
output_dir: gemma4-31b-it-glimmer-rp-r16a32
chat_template_path: chat_template_with_channel.jinja
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: 6144
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: 16
lora_alpha: 32
lora_dropout: 0.0
use_rslora: false
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: constant_with_warmup
warmup_ratio: 0.05
weight_decay: 0.0
max_grad_norm: 1.0
optim: paged_adamw_8bit
num_train_epochs: 1
saves_per_epoch: 2
save_total_limit: 4
rolling_save_steps: 30
rolling_save_total_limit: 1
assistant_only_loss: true
full_mask_reasoning: true
logging_steps: 1
disable_tqdm: false
report_to: wandb
run_name: g4-31b-it-glimmer-rp

Data config

datasets:
- path: rp_generation_final.jsonl
  type: chat
  truncation_strategy: split
- path: rp_generation_mistral.jsonl
  type: chat
  truncation_strategy: split
- path: instruct.jsonl
  type: chat
  truncation_strategy: split
- path: rp_analysis.jsonl
  type: chat
  truncation_strategy: split
- path: writing_critique.jsonl
  type: chat
  truncation_strategy: split
- path: marvin_style_bible.jsonl
  type: chat
  truncation_strategy: split
shuffle_datasets: true
shuffle_combined: true
shuffle_seed: 42
eval_split: 0
split_seed: 42
assistant_only_loss: true

Framework versions

PEFT 0.18.1
Loft: 0.1.0
Transformers: 5.5.4
Pytorch: 2.6.0+cu124
Datasets: 4.6.1
Tokenizers: 0.22.2

Downloads last month: -

Safetensors

Model size

33B params

Tensor type

BF16

Model tree for BirdToast/Gemma-4-31B-glimmer-rp-v0.1

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Adapter

(92)

this model

Adapters

2 models