Instructions to use munzurul/SPEAKLAR-RAG-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use munzurul/SPEAKLAR-RAG-1.7B with Transformers:

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

pipe = pipeline("text-generation", model="munzurul/SPEAKLAR-RAG-1.7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("munzurul/SPEAKLAR-RAG-1.7B")
model = AutoModelForCausalLM.from_pretrained("munzurul/SPEAKLAR-RAG-1.7B", 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-RAG-1.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "munzurul/SPEAKLAR-RAG-1.7B"
# 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-RAG-1.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/munzurul/SPEAKLAR-RAG-1.7B

SGLang

How to use munzurul/SPEAKLAR-RAG-1.7B 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-RAG-1.7B" \
    --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-RAG-1.7B",
		"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-RAG-1.7B" \
        --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-RAG-1.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use munzurul/SPEAKLAR-RAG-1.7B with Docker Model Runner:
```
docker model run hf.co/munzurul/SPEAKLAR-RAG-1.7B
```

SPEAKLAR-RAG-1.7B

This is a standalone full checkpoint for retrieval-augmented customer-support responses.

It is intended for retrieval-augmented customer-support responses. Supply concise, relevant retrieved evidence rather than a full policy document. The model was tested with Bengali evidence and produces Bengali answers.

Load

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "munzurul/SPEAKLAR-RAG-1.7B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

Important

This model should not be used as the source of truth for changing business facts. Retrieve relevant knowledge first, then use the retrieved evidence as model context. Validate prices, calculations, and policy-critical responses in application code.

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Safetensors

Model size

2B params

Tensor type

F16

munzurul
/

SPEAKLAR-RAG-1.7B

SPEAKLAR-RAG-1.7B

Load

Important

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