Instructions to use ardakalayci/bist-tool-gemma4-e2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ardakalayci/bist-tool-gemma4-e2b with Transformers:

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

pipe = pipeline("text-generation", model="ardakalayci/bist-tool-gemma4-e2b")
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("ardakalayci/bist-tool-gemma4-e2b")
model = AutoModelForMultimodalLM.from_pretrained("ardakalayci/bist-tool-gemma4-e2b", 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 ardakalayci/bist-tool-gemma4-e2b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ardakalayci/bist-tool-gemma4-e2b

SGLang

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

Unsloth Desktop
Docker Model Runner
How to use ardakalayci/bist-tool-gemma4-e2b with Docker Model Runner:
```
docker model run hf.co/ardakalayci/bist-tool-gemma4-e2b
```

BIST Tool — Gemma 4 E2B

Fine-tuned Gemma 4 E2B for financial tool-calling (BIST / US / FX / crypto / funds-style tools).

Base: unsloth/gemma-4-E2B-it
Training: Unsloth Studio, Gemma-4 chat template, thinking disabled
Data: bist-tool-sft (update username after upload)

Behavior

Intent	Tools
Known ticker price (`GARAN`, `THYAO`, `AAPL`)	`get_quote`
Research a known stock	`get_quote` + `get_profile` + `get_technical_analysis`
Unknown name (`EUREN`)	4× `search_symbol` (bist, us, fund, crypto) — not EUR/FX
Explicit FX (`euro kuru`, `dolar kaç`)	`get_quote` with `market: fx`

Example (Gemma 4 tool tokens):

<|tool_call>call:get_quote{symbol:<|"|>THYAO<|"|>,market:<|"|>bist<|"|>}<tool_call|>

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

REPO = "HF_USER/bist-tool-gemma4-e2b"

tokenizer = AutoTokenizer.from_pretrained(REPO)
model = AutoModelForCausalLM.from_pretrained(
    REPO,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

Use the Gemma 4 chat template. Prefer thinking off for clean tool tokens.
If the host prefixes tool names (mcp__…__get_quote), map them to short labels (get_quote, search_symbol, …).

License

Derived from Gemma weights; subject to the Gemma license and terms.

Downloads last month: 3

Safetensors

Model size

5B params

Tensor type

BF16

Model tree for ardakalayci/bist-tool-gemma4-e2b

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

unsloth/gemma-4-E2B-it

Finetuned

(176)

this model