Instructions to use hishab/titulm-gemma-2-2b-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hishab/titulm-gemma-2-2b-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="hishab/titulm-gemma-2-2b-v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hishab/titulm-gemma-2-2b-v1.0")
model = AutoModelForCausalLM.from_pretrained("hishab/titulm-gemma-2-2b-v1.0", 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-gemma-2-2b-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hishab/titulm-gemma-2-2b-v1.0"
# 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-gemma-2-2b-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hishab/titulm-gemma-2-2b-v1.0

SGLang

How to use hishab/titulm-gemma-2-2b-v1.0 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-gemma-2-2b-v1.0" \
    --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-gemma-2-2b-v1.0",
		"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-gemma-2-2b-v1.0" \
        --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-gemma-2-2b-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hishab/titulm-gemma-2-2b-v1.0 with Docker Model Runner:
```
docker model run hf.co/hishab/titulm-gemma-2-2b-v1.0
```

sagorsarker commited on Oct 24, 2024

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@@ -113,6 +113,7 @@ We evaluated the models on the following datasets:
 #### Evaluation on English Benchmark datasets
 - **gemma-2-2b** outperforms **titulm-gemma-2-2b-v1.0** across all tasks in both 0-shot and 5-shot settings, achieving the highest scores in **MMLU**, **BoolQ**, **Commonsense QA**, **OpenBook QA**, and **PIQA**, with a peak 5-shot score of **0.80** in **PIQA**.
 - **titulm-gemma-2-2b-v1.0** shows competitive performance but lags behind **gemma-2-2b**, particularly in **Commonsense QA** and **BoolQ**, with the highest score being **0.77** in **PIQA**.
 | Model                                | Shots  | MMLU         | BoolQ      | Commonsense QA     | OpenBook QA     | PIQA      |
 |--------------------------------------|--------|--------------|------------|--------------------|-----------------|-----------|

 #### Evaluation on English Benchmark datasets
 - **gemma-2-2b** outperforms **titulm-gemma-2-2b-v1.0** across all tasks in both 0-shot and 5-shot settings, achieving the highest scores in **MMLU**, **BoolQ**, **Commonsense QA**, **OpenBook QA**, and **PIQA**, with a peak 5-shot score of **0.80** in **PIQA**.
 - **titulm-gemma-2-2b-v1.0** shows competitive performance but lags behind **gemma-2-2b**, particularly in **Commonsense QA** and **BoolQ**, with the highest score being **0.77** in **PIQA**.
+- It is expected as we have trained our model only on Bangla text.
 | Model                                | Shots  | MMLU         | BoolQ      | Commonsense QA     | OpenBook QA     | PIQA      |
 |--------------------------------------|--------|--------------|------------|--------------------|-----------------|-----------|