Instructions to use hishab/titulm-llama-3.2-3b-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hishab/titulm-llama-3.2-3b-v2.0 with Transformers:

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

pipe = pipeline("text-generation", model="hishab/titulm-llama-3.2-3b-v2.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hishab/titulm-llama-3.2-3b-v2.0")
model = AutoModelForCausalLM.from_pretrained("hishab/titulm-llama-3.2-3b-v2.0", 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 hishab/titulm-llama-3.2-3b-v2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hishab/titulm-llama-3.2-3b-v2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hishab/titulm-llama-3.2-3b-v2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hishab/titulm-llama-3.2-3b-v2.0

SGLang

How to use hishab/titulm-llama-3.2-3b-v2.0 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 "hishab/titulm-llama-3.2-3b-v2.0" \
    --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": "hishab/titulm-llama-3.2-3b-v2.0",
		"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 "hishab/titulm-llama-3.2-3b-v2.0" \
        --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": "hishab/titulm-llama-3.2-3b-v2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hishab/titulm-llama-3.2-3b-v2.0 with Docker Model Runner:
```
docker model run hf.co/hishab/titulm-llama-3.2-3b-v2.0
```

sagorsarker commited on Jun 7, 2025

Commit

ce14af7

verified ·

1 Parent(s): 5922c88

Update README.md

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@@ -137,4 +137,17 @@ We evaluated the models on the following datasets:
 ### Intended Use
 - Bangla text generation
 - Bangla language understanding tasks
-- Bangla instruction fine-tuning tasks

 ### Intended Use
 - Bangla text generation
 - Bangla language understanding tasks
+- Bangla instruction fine-tuning tasks
+## Citation
+```
+@misc{nahin2025titullmsfamilybanglallms,
+      title={TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking},
+      author={Shahriar Kabir Nahin and Rabindra Nath Nandi and Sagor Sarker and Quazi Sarwar Muhtaseem and Md Kowsher and Apu Chandraw Shill and Md Ibrahim and Mehadi Hasan Menon and Tareq Al Muntasir and Firoj Alam},
+      year={2025},
+      eprint={2502.11187},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.11187},
+}
+```