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

nielsr HF Staff commited on Feb 24, 2025

Commit

5922c88

verified ·

1 Parent(s): d819e5d

Add link to paper (#1)

Browse files

- Add link to paper (96ae738dfe04d453831fbedd8f6fe09030a9d329)

Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +4 -3

README.md CHANGED Viewed

@@ -2,6 +2,7 @@
 language:
 - bn
 library_name: transformers
 pipeline_tag: text-generation
 tags:
 - hishab
@@ -10,7 +11,6 @@ tags:
 - llama
 - llama-3
 - llama-factory
-license: llama3.2
 ---
 ## Model Information
@@ -33,6 +33,8 @@ This model is a continually pretrained version of the [meta-llama/Llama-3.2-3B](
 **License:** We are using a similar license to Llama 3.2. Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
 ## How to use
 - Use with transformers
@@ -135,5 +137,4 @@ We evaluated the models on the following datasets:
 ### Intended Use
 - Bangla text generation
 - Bangla language understanding tasks
-- Bangla instruction fine-tuning tasks

 language:
 - bn
 library_name: transformers
+license: llama3.2
 pipeline_tag: text-generation
 tags:
 - hishab
 - llama
 - llama-3
 - llama-factory
 ---
 ## Model Information
 **License:** We are using a similar license to Llama 3.2. Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
+Paper: [TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking](https://huggingface.co/papers/2502.11187)
+<br>
 ## How to use
 - Use with transformers
 ### Intended Use
 - Bangla text generation
 - Bangla language understanding tasks
+- Bangla instruction fine-tuning tasks