Instructions to use electroglyph/gemma4-e4b-sft-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use electroglyph/gemma4-e4b-sft-checkpoints with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("electroglyph/gemma4-e4b-ftpo-merged")
model = PeftModel.from_pretrained(base_model, "electroglyph/gemma4-e4b-sft-checkpoints")

Transformers

How to use electroglyph/gemma4-e4b-sft-checkpoints with Transformers:

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

pipe = pipeline("text-generation", model="electroglyph/gemma4-e4b-sft-checkpoints")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("electroglyph/gemma4-e4b-sft-checkpoints", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use electroglyph/gemma4-e4b-sft-checkpoints with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "electroglyph/gemma4-e4b-sft-checkpoints"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "electroglyph/gemma4-e4b-sft-checkpoints",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/electroglyph/gemma4-e4b-sft-checkpoints

SGLang

How to use electroglyph/gemma4-e4b-sft-checkpoints 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 "electroglyph/gemma4-e4b-sft-checkpoints" \
    --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": "electroglyph/gemma4-e4b-sft-checkpoints",
		"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 "electroglyph/gemma4-e4b-sft-checkpoints" \
        --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": "electroglyph/gemma4-e4b-sft-checkpoints",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use electroglyph/gemma4-e4b-sft-checkpoints with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for electroglyph/gemma4-e4b-sft-checkpoints to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for electroglyph/gemma4-e4b-sft-checkpoints to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for electroglyph/gemma4-e4b-sft-checkpoints to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="electroglyph/gemma4-e4b-sft-checkpoints",
    max_seq_length=2048,
)

Docker Model Runner
How to use electroglyph/gemma4-e4b-sft-checkpoints with Docker Model Runner:
```
docker model run hf.co/electroglyph/gemma4-e4b-sft-checkpoints
```

gemma4-e4b-sft-checkpoints

File size: 1,672 Bytes

42352eb

{
  "alora_invocation_tokens": null,
  "alpha_pattern": {},
  "arrow_config": null,
  "auto_mapping": {
    "base_model_class": "Gemma4ForConditionalGeneration",
    "parent_library": "transformers.models.gemma4.modeling_gemma4",
    "unsloth_fixed": true
  },
  "base_model_name_or_path": "electroglyph/gemma4-e4b-ftpo-merged",
  "bias": "none",
  "corda_config": null,
  "ensure_weight_tying": false,
  "eva_config": null,
  "exclude_modules": null,
  "fan_in_fan_out": false,
  "inference_mode": true,
  "init_lora_weights": true,
  "layer_replication": null,
  "layers_pattern": null,
  "layers_to_transform": null,
  "loftq_config": {},
  "lora_alpha": 128,
  "lora_bias": false,
  "lora_dropout": 0,
  "lora_ga_config": null,
  "megatron_config": null,
  "megatron_core": "megatron.core",
  "modules_to_save": [
    "lm_head"
  ],
  "peft_type": "LORA",
  "peft_version": "0.19.1",
  "qalora_group_size": 16,
  "r": 128,
  "rank_pattern": {},
  "revision": null,
  "target_modules": "(?:.*?(?:language|text).*?(?:self_attn|attention|attn|mixer|mlp|feed_forward|ffn|dense|mixer).*?(?:k_proj|q_proj|v_proj|o_proj|gate_proj|up_proj|down_proj|per_layer_input_gate|per_layer_projection|linear|embedding_projection|relative_k_proj))|(?:\\bmodel\\.layers\\.[\\d]{1,}\\.(?:self_attn|attention|attn|mixer|mlp|feed_forward|ffn|dense|mixer)\\.(?:(?:k_proj|q_proj|v_proj|o_proj|gate_proj|up_proj|down_proj|per_layer_input_gate|per_layer_projection|linear|embedding_projection|relative_k_proj)))",
  "target_parameters": null,
  "task_type": "CAUSAL_LM",
  "trainable_token_indices": null,
  "use_bdlora": null,
  "use_dora": false,
  "use_qalora": false,
  "use_rslora": false
}