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JTS-AI
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Model Details
Model Provider
JTS-AI
Model Tree
Qwen/Qwen3-14B
Input Modalities
Output Modalities
Supported Functionality
| Benchmark/Model | OpenJAI-v1.0-14b | Qwen3-14b | Typhoon2.1-gemma3-12b | OpenThaiGPT1.5-14b | GPT-4.1-nano-2025-04-14 |
|---|---|---|---|---|---|
| Instruction Following | |||||
| IFBench-EN | 32.4 | 29.7 | 27.4 | 30.6 | 28.3 |
| IFBench-TH | 39.4 | 38.1 | 36.5 | 35.4 | 34.9 |
| Multi-turn Capability | |||||
| MT-Bench-EN | 8.4 | 8.4 | 8.3 | 7.8 | 8.5 |
| MT-Bench-TH | 8.1 | 8.0 | 8.1 | 6.9 | 8.0 |
| Long-context Understanding | |||||
| MRCR | 18.9 | 18.3 | 16.9 | 16.9 | 16.2 |
| LongBench-v2 | 33.6 | 32.4 | 29.2 | 33.6 | 28.8 |
| Tool Calling | |||||
| BFCL-v3-EN | 60.5 | 59.2 | 52.2 | 52.9 | 53.1 |
| BFCL-v3-TH | 47.0 | 46.0 | 45.0 | 44.9 | 41.1 |
| General Knowledge | |||||
| MMLU-ProX-lite-EN | 66.0 | 66.6 | 55.1 | 64.3 | 36.3 |
| MMLU-ProX-lite-TH | 54.7 | 57.5 | 45.2 | 49.3 | 39.8 |
To get started, we recommend using the latest version of the Hugging Face transformers library.
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "JTS-AI/OpenJAI-v1.0-14B" # load the tokenizer and the modeltokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto") # prepare the model inputprompt = "แนะนำที่เที่ยวแถวสยามหน่อย"messages = [ {"role": "user", "content": prompt}]text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.)model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completiongenerated_ids = model.generate( **model_inputs, max_new_tokens=1024)output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n") print("Content:", content)[!NOTE] OpenJAI-v1.0 is optimized for non-thinking mode. While the base model's thinking mode may be accessible, its performance is not guaranteed.
OpenJAI-v1.0 has strong tool-calling capabilities. You can use frameworks like Qwen-Agent by adapting the model configuration.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
python
from qwen_agent.agents import Assistant # Define LLM, pointing to your OpenJAI-v1.0 endpointllm_cfg = { 'model': 'JTS-AI/OpenJAI-v1.0-14B', # Use a custom endpoint compatible with OpenAI API: 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY',} # Define Toolstools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools] # Define Agentbot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generationmessages = [{'role': 'user', 'content': 'วาดกราฟแสดงราคาหุ้นของ JTS ในช่วง 1 เดือนที่ผ่านมา'}]for responses in bot.run(messages=messages): passprint(responses)OpenJAI-v1.0 was trained for robust performance on input context lengths up to 120,000 tokens. The model operates optimally within its native 32,768-token window. To process contexts exceeding this limit, applying a context extension technique like YaRN or Dynamic RoPE scaling is necessary.
Frameworks like vLLM and SGLang support passing command-line arguments to enable RoPE scaling.
For vllm, you can use
shell
vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072 For sglang, you can use
shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'[!NOTE] All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the
rope_scalingconfiguration only when processing long contexts is required. It is also recommended to modify thefactoras needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to setfactoras 2.0.
[!NOTE] The default
max_position_embeddingsinconfig.jsonis set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
text
@misc{trakuekul2025openjaiv10openthailarge, title={OpenJAI-v1.0: An Open Thai Large Language Model}, author={Pontakorn Trakuekul and Attapol T. Rutherford and Jullajak Karnjanaekarin and Narongkorn Panitsrisit and Sumana Sumanakul}, year={2025}, eprint={2510.06847}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2510.06847}, }from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "JTS-AI/OpenJAI-v1.0-14B" # load the tokenizer and the modeltokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto") # prepare the model inputprompt = "แนะนำที่เที่ยวแถวสยามหน่อย"messages = [ {"role": "user", "content": prompt}]text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.)model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completiongenerated_ids = model.generate( **model_inputs, max_new_tokens=1024)output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n") print("Content:", content)from qwen_agent.agents import Assistant # Define LLM, pointing to your OpenJAI-v1.0 endpointllm_cfg = { 'model': 'JTS-AI/OpenJAI-v1.0-14B', # Use a custom endpoint compatible with OpenAI API: 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY',} # Define Toolstools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools] # Define Agentbot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generationmessages = [{'role': 'user', 'content': 'วาดกราฟแสดงราคาหุ้นของ JTS ในช่วง 1 เดือนที่ผ่านมา'}]for responses in bot.run(messages=messages): passprint(responses)vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072 python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'@misc{trakuekul2025openjaiv10openthailarge, title={OpenJAI-v1.0: An Open Thai Large Language Model}, author={Pontakorn Trakuekul and Attapol T. Rutherford and Jullajak Karnjanaekarin and Narongkorn Panitsrisit and Sumana Sumanakul}, year={2025}, eprint={2510.06847}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2510.06847}, }