--- language: - th - en license: apache-2.0 library_name: transformers base_model: - Qwen/Qwen2.5-7B-Instruct - Qwen/Qwen2.5-7B pipeline_tag: text-generation model-index: - name: Tsunami-0.5x-7B-Instruct results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 70.99 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Tsunami-th/Tsunami-0.5x-7B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 37.36 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Tsunami-th/Tsunami-0.5x-7B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 4.83 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Tsunami-th/Tsunami-0.5x-7B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 8.61 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Tsunami-th/Tsunami-0.5x-7B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 18.57 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Tsunami-th/Tsunami-0.5x-7B-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 38.42 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Tsunami-th/Tsunami-0.5x-7B-Instruct name: Open LLM Leaderboard --- Tsunami Model # Tsunami-0.5x-7B-Instruct **TSUNAMI**: Transformative Semantic Understanding and Natural Augmentation Model for Intelligence. **TSUNAMI** full name was created by ChatGPT. --- ### infomation **Tsunami-0.5x-7B-Instruct** is Thai Large Language Model that fine-tuned from **Qwen2.5-7B** around **100,000** rows in Thai dataset. --- ### Prompt Template This model uses `ChatML` prompt template: ``` <|im_start|>system {System}<|im_end|> <|im_start|>user {User}<|im_end|> <|im_start|>assistant {Assistant} ```` ### How to use ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_name = "Tsunami-th/Tsunami-0.5x-7B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "สวัสดีครับ"} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = tokenizer(text, return_tensors="pt") inputs = inputs.to(model.device) with torch.no_grad(): output = model.generate(**inputs, max_new_tokens=512) response = tokenizer.decode(output[0, len(inputs['input_ids'][0]):], skip_special_tokens=True) ``` --- ### Author - Pollakrit Lorprasertkul | game.pollakrit@gmail.com --- - **Tsunami-0.5x-7B-Instruct** is the version 0.5x that did not train on the whole dataset. - **Tsunami-1.0-7B-Instruct** is coming soon. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Tsunami-th__Tsunami-0.5x-7B-Instruct) | Metric |Value| |-------------------|----:| |Avg. |29.80| |IFEval (0-Shot) |70.99| |BBH (3-Shot) |37.36| |MATH Lvl 5 (4-Shot)| 4.83| |GPQA (0-shot) | 8.61| |MuSR (0-shot) |18.57| |MMLU-PRO (5-shot) |38.42|