Instructions to use munzurul/speaklar_gemma-3-1b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use munzurul/speaklar_gemma-3-1b-it with Transformers:

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

pipe = pipeline("text-generation", model="munzurul/speaklar_gemma-3-1b-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("munzurul/speaklar_gemma-3-1b-it")
model = AutoModelForCausalLM.from_pretrained("munzurul/speaklar_gemma-3-1b-it", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use munzurul/speaklar_gemma-3-1b-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "munzurul/speaklar_gemma-3-1b-it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "munzurul/speaklar_gemma-3-1b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/munzurul/speaklar_gemma-3-1b-it

SGLang

How to use munzurul/speaklar_gemma-3-1b-it 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 "munzurul/speaklar_gemma-3-1b-it" \
    --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": "munzurul/speaklar_gemma-3-1b-it",
		"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 "munzurul/speaklar_gemma-3-1b-it" \
        --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": "munzurul/speaklar_gemma-3-1b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use munzurul/speaklar_gemma-3-1b-it with Docker Model Runner:
```
docker model run hf.co/munzurul/speaklar_gemma-3-1b-it
```

Speaklar Gemma 3 1B IT

A Gemma 3 1B instruction model fine-tuned for grounded Bengali voice-bot conversations.

Intended use

Provide the relevant knowledge-base context with each request. The model is trained to answer from supplied evidence, state when information is unavailable, follow Bengali voice-agent style constraints, gather essential details for orders and appointments, and safely hand off sensitive or unsupported requests.

Grounding is a product-level responsibility: retrieve the correct context, enforce output checks for important workflows, and evaluate on your real traffic before production deployment.

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "munzurul/speaklar_gemma-3-1b-it"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
messages = [{"role": "user", "content": "প্রশ্ন: আপনার সেবা কী?\n\nপ্রসঙ্গ: ..."}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True,
                                       return_tensors="pt", return_dict=True).to(model.device)
answer_ids = model.generate(**inputs, max_new_tokens=160, do_sample=False)
print(tokenizer.decode(answer_ids[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Training data

Fine-tuned with QLoRA on a 15,000-example Bengali voice-bot behavior dataset. The set includes Bengali, English, and Banglish inputs/context; assistant outputs are Bengali-only. It covers evidence-grounded answers, abstention, calculations, order/appointment intake, payment safety, complaint intake, and human handoff.

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Safetensors

Model size

1.0B params

Tensor type

F16