Instructions to use Timegravity/tamil-lm-2b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Timegravity/tamil-lm-2b-instruct with Transformers:

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

pipe = pipeline("image-text-to-text", model="Timegravity/tamil-lm-2b-instruct")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Timegravity/tamil-lm-2b-instruct")
model = AutoModelForMultimodalLM.from_pretrained("Timegravity/tamil-lm-2b-instruct", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Timegravity/tamil-lm-2b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Timegravity/tamil-lm-2b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Timegravity/tamil-lm-2b-instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Timegravity/tamil-lm-2b-instruct

SGLang

How to use Timegravity/tamil-lm-2b-instruct 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 "Timegravity/tamil-lm-2b-instruct" \
    --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": "Timegravity/tamil-lm-2b-instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Timegravity/tamil-lm-2b-instruct" \
        --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": "Timegravity/tamil-lm-2b-instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Timegravity/tamil-lm-2b-instruct with Docker Model Runner:
```
docker model run hf.co/Timegravity/tamil-lm-2b-instruct
```

vignesh1234 commited on 17 days ago

Commit

e6b8aaf

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1 Parent(s): b522de9

Model card and records for the 0.1.0-preview release

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@@ -22,6 +22,7 @@ tamil-lm-2b is Qwen/Qwen3.5-2B-Base adapted to Tamil by continued pretraining, a
 - Languages: Tamil, Tanglish, English.
 - Precision: bf16 weights. Trained on a single 48 GB GPU.
 - Licence: Apache License 2.0 (see Licence and attribution).
 - Related: quantised GGUF files and the knowledge packs are in https://huggingface.co/Timegravity/tamil-lm-2b-gguf; the serving stack, evaluation harness, training scripts and knowledge base build scripts are in https://github.com/timegravity/tamil-lm; the Timegravity Tamil Android app is released from that repository.
 ## Intended use

 - Languages: Tamil, Tanglish, English.
 - Precision: bf16 weights. Trained on a single 48 GB GPU.
 - Licence: Apache License 2.0 (see Licence and attribution).
+- Models built with this recipe: https://github.com/timegravity/tamil-lm/blob/main/BUILT-WITH-THIS-RECIPE.md (a self-service list; listed models are their builders' own work, not reviewed or endorsed by Timegravity Labs).
 - Related: quantised GGUF files and the knowledge packs are in https://huggingface.co/Timegravity/tamil-lm-2b-gguf; the serving stack, evaluation harness, training scripts and knowledge base build scripts are in https://github.com/timegravity/tamil-lm; the Timegravity Tamil Android app is released from that repository.
 ## Intended use