Instructions to use myyycroft/Gemma-4-E2B-AmbigQA-full-member-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use myyycroft/Gemma-4-E2B-AmbigQA-full-member-4 with Transformers:

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

pipe = pipeline("text-generation", model="myyycroft/Gemma-4-E2B-AmbigQA-full-member-4")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("myyycroft/Gemma-4-E2B-AmbigQA-full-member-4")
model = AutoModelForMultimodalLM.from_pretrained("myyycroft/Gemma-4-E2B-AmbigQA-full-member-4", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use myyycroft/Gemma-4-E2B-AmbigQA-full-member-4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "myyycroft/Gemma-4-E2B-AmbigQA-full-member-4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "myyycroft/Gemma-4-E2B-AmbigQA-full-member-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/myyycroft/Gemma-4-E2B-AmbigQA-full-member-4

SGLang

How to use myyycroft/Gemma-4-E2B-AmbigQA-full-member-4 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 "myyycroft/Gemma-4-E2B-AmbigQA-full-member-4" \
    --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": "myyycroft/Gemma-4-E2B-AmbigQA-full-member-4",
		"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 "myyycroft/Gemma-4-E2B-AmbigQA-full-member-4" \
        --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": "myyycroft/Gemma-4-E2B-AmbigQA-full-member-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use myyycroft/Gemma-4-E2B-AmbigQA-full-member-4 with Docker Model Runner:
```
docker model run hf.co/myyycroft/Gemma-4-E2B-AmbigQA-full-member-4
```

myyycroft/Gemma-4-E2B-AmbigQA-full-member-4

Ensemble member 4 (seed 4078) from run gemma4_e2b_full_small_lr.

Fine-tuned from google/gemma-4-E2B-it on AmbigQA.

Evaluation note

Metrics below are computed on small fixed subsets, not full benchmarks. Exact subset sizes: AmbigQA (128), IFEval (64), MMLU (228). Reported values are from the final training step (step=1620, epoch=3).

Dataset

Hub dataset: sewon/ambig_qa (config_name=light)
Revision: 6e667596df70f17ba3c8e7be4b7361f6be8b60f8
Splits: train=train, validation=validation
Dev holdout: dev_fraction=0.1, split_seed=1729
Bootstrap resampling: False

System prompt

No system prompt (dataset.system_prompt: null).

Hyperparameters

Exact resolved settings used for this run (also attached as resolved_config.yaml):

model:
  name: google/gemma-4-E2B-it
  revision: main
  dtype: bfloat16
  enable_thinking: false
dataset:
  name: sewon/ambig_qa
  config_name: light
  revision: 6e667596df70f17ba3c8e7be4b7361f6be8b60f8
  train_split: train
  validation_split: validation
  dev_fraction: 0.1
  split_seed: 1729
  bootstrap: false
  max_train_examples: null
  max_dev_examples: null
  system_prompt_set: false
adaptation:
  method: full
training:
  num_train_epochs: 3.0
  max_steps: -1
  learning_rate: 2.0e-05
  weight_decay: 0.1
  warmup_ratio: 0.03
  per_device_train_batch_size: 4
  per_device_eval_batch_size: 16
  gradient_accumulation_steps: 8
  max_seq_length: 512
  logging_steps: 10
  save_every_steps: null
  save_total_limit: null
  gradient_checkpointing: true
  bf16: true
  tf32: true
  max_grad_norm: 1.0
  dataloader_num_workers: 2
  adam_beta1: 0.9
  adam_beta2: 0.999
  adam_epsilon: 1.0e-08
ensemble:
  size: 5
  base_seed: 42
  this_member_index: 4
  this_member_seed: 4078
evaluation_subsets:
  ambigqa:
    subset_size: 128
    seed: 1001
  ifeval:
    subset_size: 64
    seed: 1002
  mmlu:
    subset_size: 228
    seed: 1004

Metrics breakdown

The metrics in this section use small fixed subsets, not full benchmarks: AmbigQA (128), IFEval (64), MMLU (228).

Final-step ensemble mean ± std (n=5)

AmbigQA (128) accuracy: 0.0922 ± 0.0116
AmbigQA (128) AlignScore: 0.1887 ± 0.0125
IFEval (64) prompt_level_strict_accuracy: 0.6250 ± 0.0442
MMLU (228) accuracy: 0.5596 ± 0.0200
train_loss: 1.5896 ± 0.0063
steps: 1620
epochs: 3

Per-member (final step)

member	seed	steps	epochs	train_loss	AmbigQA (128) acc	AmbigQA (128) AlignScore	IFEval (64) strict	MMLU (228) acc
0	42	1620	3	1.5835	0.0781	0.1918	0.6719	0.5526
1	1051	1620	3	1.5954	0.0859	0.1729	0.5781	0.5526
2	2060	1620	3	1.5892	0.0938	0.1924	0.6719	0.5702
3	3069	1620	3	1.5833	0.1094	0.1805	0.6094	0.5877
4 (this repo)	4078	1620	3	1.5964	0.0938	0.2057	0.5938	0.5351

Files

Model weights / adapter files from members/member_004/final/
resolved_config.yaml — full resolved training config
ensemble_metrics.png — train/eval curves for the whole ensemble
run_artifacts/ — ensemble-level manifests, status, and captured environment
run_artifacts/members/member_004/ — this member's manifests, metadata, and status
run_artifacts/members/member_004/predictions/ — per-dataset JSONL predictions from every intermediate evaluation step