Instructions to use Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Puujeeeeeeeeeeee/gemma4-e4b-cpt-round1")
model = PeftModel.from_pretrained(base_model, "Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter")

Transformers

How to use Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter with Transformers:

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

pipe = pipeline("text-generation", model="Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter

SGLang

How to use Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter 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 "Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter with Docker Model Runner:
```
docker model run hf.co/Puujeeeeeeeeeeee/gemma4-e4b-cpt-round2-adapter
```

gemma4-e4b-cpt-round2-adapter / tokenizer_config.json

Puujeeeeeeeeeeee

Upload model trained with Unsloth

8d13e00 verified about 2 months ago

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	{
	"audio_token": "<\|audio\|>",
	"backend": "tokenizers",
	"boa_token": "<\|audio>",
	"boi_token": "<\|image>",
	"bos_token": "<bos>",
	"eoa_token": "<audio\|>",
	"eoc_token": "<channel\|>",
	"eoi_token": "<image\|>",
	"eos_token": "<eos>",
	"eot_token": "<turn\|>",
	"escape_token": "<\|\"\|>",
	"etc_token": "<tool_call\|>",
	"etd_token": "<tool\|>",
	"etr_token": "<tool_response\|>",
	"extra_special_tokens": [
	"<\|video\|>"
	],
	"image_token": "<\|image\|>",
	"is_local": false,
	"local_files_only": false,
	"mask_token": "<mask>",
	"max_length": 1024,
	"model_max_length": 131072,
	"model_specific_special_tokens": {
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	"etd_token": "<tool\|>",
	"etr_token": "<tool_response\|>",
	"image_token": "<\|image\|>",
	"soc_token": "<\|channel>",
	"sot_token": "<\|turn>",
	"stc_token": "<\|tool_call>",
	"std_token": "<\|tool>",
	"str_token": "<\|tool_response>",
	"think_token": "<\|think\|>"
	},
	"pad_token": "<pad>",
	"padding_side": "right",
	"processor_class": "Gemma4Processor",
	"soc_token": "<\|channel>",
	"sot_token": "<\|turn>",
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	"100": {
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