Instructions to use TarunNagaSai007/gemma4-e2b-pokemon-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TarunNagaSai007/gemma4-e2b-pokemon-merged with Transformers:

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

pipe = pipeline("text-generation", model="TarunNagaSai007/gemma4-e2b-pokemon-merged")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("TarunNagaSai007/gemma4-e2b-pokemon-merged")
model = AutoModelForMultimodalLM.from_pretrained("TarunNagaSai007/gemma4-e2b-pokemon-merged", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TarunNagaSai007/gemma4-e2b-pokemon-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TarunNagaSai007/gemma4-e2b-pokemon-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TarunNagaSai007/gemma4-e2b-pokemon-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TarunNagaSai007/gemma4-e2b-pokemon-merged

SGLang

How to use TarunNagaSai007/gemma4-e2b-pokemon-merged 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 "TarunNagaSai007/gemma4-e2b-pokemon-merged" \
    --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": "TarunNagaSai007/gemma4-e2b-pokemon-merged",
		"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 "TarunNagaSai007/gemma4-e2b-pokemon-merged" \
        --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": "TarunNagaSai007/gemma4-e2b-pokemon-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use TarunNagaSai007/gemma4-e2b-pokemon-merged with Docker Model Runner:
```
docker model run hf.co/TarunNagaSai007/gemma4-e2b-pokemon-merged
```

Gemma 4 E2B — Pokémon Pokédex (Merged 16-bit)

Standalone merged model: google/gemma-4-e2b-it fine-tuned on Pokémon data with the LoRA adapter folded into the base weights. No separate adapter needed — load and run directly. For the lightweight adapter version, see TarunNagaSai007/gemma4-e2b-pokemon.

Model Details

Base model: google/gemma-4-e2b-it
Method: LoRA fine-tune merged to 16-bit, via Unsloth
Format: Full safetensors (~9.5GB), fp16

Tasks

Task	Input example	Output
Stat	"What is the Speed of Duskull?"	`25`
Profile	"Tell me about Miltank."	Full Pokédex entry
Battle	"If Raichu battles Blastoise, who wins?"	`<think>` reasoning + verdict

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "TarunNagaSai007/gemma4-e2b-pokemon-merged",
    torch_dtype=torch.float16, device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("TarunNagaSai007/gemma4-e2b-pokemon-merged")

messages = [
    {"role": "system", "content": [{"type": "text", "text": "You are a Pokédex assistant. Answer questions about Pokémon stats, profiles, and battle outcomes accurately."}]},
    {"role": "user", "content": [{"type": "text", "text": "What is the Speed of Pikachu?"}]},
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(**{"input_ids": inputs}, max_new_tokens=256)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Training

Dataset: ~8,600 instruction examples (stat / profile / battle), 957 validation, 1,200 test
Epochs: 3 | Effective batch: 8 | LR: 2e-4 cosine
Optimizer: adamw_8bit | Hardware: single T4 (Colab)
Final validation loss: 0.164

Conversion to GGUF

This merged model can be converted to GGUF for Ollama / llama.cpp. Use the Gemma 4 chat template when running.

Limitations

Trained on a fixed Pokédex snapshot. Battle reasoning uses a simplified type/offense/speed heuristic, not full damage mechanics. Educational/hobby use.

Downloads last month: 18

Safetensors

Model size

5B params

Tensor type

BF16