Instructions to use Timegravity/tamil-lm-2b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download eval_results/artifacts.json from Timegravity/tamil-lm-2b-instruct: direct link, hf CLI and curl.
- Browser
- Download file 3.03 kB
-
https://huggingface.co/Timegravity/tamil-lm-2b-instruct/resolve/main/eval_results/artifacts.json
- Command line
-
hf download hf://Timegravity/tamil-lm-2b-instruct/eval_results/artifacts.json
-
curl -L -o artifacts.json https://huggingface.co/Timegravity/tamil-lm-2b-instruct/resolve/main/eval_results/artifacts.json
3.03 kB
| { | |
| "generated": "2026-09-15 06:44 UTC", | |
| "letter_position": { | |
| "model": "Qwen3.5-2B", | |
| "wrong_milu_answers": 4822, | |
| "most_chosen_letter": "C", | |
| "times": 4163, | |
| "mmlu_chat_before": 0.2525, | |
| "mmlu_raw": 0.49, | |
| "milu_chat_before": 0.2433, | |
| "mmlu_chat_after": 0.26, | |
| "milu_chat_after": 0.242 | |
| }, | |
| "sdpa": { | |
| "model": "Gemma-3-1B", | |
| "tasks": { | |
| "in22gen_ta_en": { | |
| "single": 39.0717, | |
| "batched_sdpa": 36.4533, | |
| "batched_eager": 39.529 | |
| }, | |
| "flores_ta_en": { | |
| "single": 40.3854, | |
| "batched_sdpa": 38.4472, | |
| "batched_eager": 40.8063 | |
| } | |
| } | |
| }, | |
| "start_token": { | |
| "material": [ | |
| "Gemma-3-1B-it: dev mmlu_en 21.0 to 41.0, test milu_ta 26.8 to 31.4, test mmlu_en 23.5 to 39.0", | |
| "Llama-3.2-1B-Instruct: dev milu_ta 26.0 to 28.0, dev mmlu_en 39.0 to 35.0, test mmlu_en 39.0 to 35.5", | |
| "Sarvam-1: dev milu_ta 34.0 to 36.7, dev mmlu_en 43.0 to 45.0" | |
| ], | |
| "pending": [], | |
| "gemma4_e4b_raw_dev_before": { | |
| "flores_en_ta_chrf": 0.0346, | |
| "flores_ta_en_chrf": 1.025, | |
| "tamil_bpc": 5.1868, | |
| "mmlu": 0.31 | |
| } | |
| }, | |
| "preamble": { | |
| "local_chat": [ | |
| { | |
| "model": "tamil-lm-2b-instruct-r4", | |
| "share": 0.0, | |
| "largest_gap": 0.0, | |
| "task": "in22gen_ta_en", | |
| "first": 48.8408, | |
| "extracted": 48.8408 | |
| }, | |
| { | |
| "model": "Qwen3.5-2B", | |
| "share": 7.8, | |
| "largest_gap": 0.4, | |
| "task": "flores_en_ta", | |
| "first": 27.9656, | |
| "extracted": 28.4019 | |
| }, | |
| { | |
| "model": "Qwen3.5-4B", | |
| "share": 1.6, | |
| "largest_gap": 0.2, | |
| "task": "in22gen_en_ta", | |
| "first": 35.8439, | |
| "extracted": 36.0683 | |
| }, | |
| { | |
| "model": "Gemma-3-1B-it", | |
| "share": 55.0, | |
| "largest_gap": 21.1, | |
| "task": "flores_en_ta", | |
| "first": 7.0102, | |
| "extracted": 28.082 | |
| }, | |
| { | |
| "model": "Llama-3.2-1B-Instruct", | |
| "share": 0.3, | |
| "largest_gap": 0.2, | |
| "task": "in22gen_ta_en", | |
| "first": 33.1526, | |
| "extracted": 33.3336 | |
| }, | |
| { | |
| "model": "Gemma-3-4B-it", | |
| "share": 89.1, | |
| "largest_gap": 44.8, | |
| "task": "flores_en_ta", | |
| "first": 1.3475, | |
| "extracted": 46.1687 | |
| }, | |
| { | |
| "model": "Sarvam-1", | |
| "share": 0.6, | |
| "largest_gap": 0.1, | |
| "task": "in22gen_en_ta", | |
| "first": 30.4741, | |
| "extracted": 30.5594 | |
| }, | |
| { | |
| "model": "Qwen3-1.7B-tamil-Instruct", | |
| "share": 18.7, | |
| "largest_gap": 1.2, | |
| "task": "in22gen_en_ta", | |
| "first": 15.264, | |
| "extracted": 16.4391 | |
| } | |
| ], | |
| "gemini": { | |
| "share": 56.3, | |
| "in22gen_ta_en_first": 24.1883, | |
| "in22gen_ta_en_extracted": 59.448 | |
| } | |
| }, | |
| "caps": { | |
| "llama_flores_refs_over_160": 725, | |
| "flores_items": 1012, | |
| "llama3b_chat_translations_cut_mid_character": 358, | |
| "captured": 1012, | |
| "llama1b_dev_indicqa_contains_before": 0.1569, | |
| "llama1b_dev_indicqa_contains_after": 0.2471, | |
| "gemini_gsm8k_cut_at_256": 72, | |
| "gemini_gsm8k_before": 0.5125, | |
| "gemini_gsm8k_after": 0.8313 | |
| }, | |
| "gsm8k_numeric": { | |
| "correct_answers_marked_wrong": 45 | |
| }, | |
| "bpc_first_token": { | |
| "our_model_share_of_characters_counted_before": { | |
| "tamil_heldout": 98.0, | |
| "tanglish_heldout": 96.4 | |
| } | |
| } | |
| } |