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aungkomyint
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Model Details
Model Provider
aungkomyint
Model Tree
aungkomyint/tara1.1
Input Modalities
Output Modalities
Supported Functionality
tara1.2-questtara-1.2-quest-assistant-7c-json-base4v2LlamaForCausalLMsafetensorsUse this model to generate exploratory questions from a topic or short prompt.
Examples:
cookinghow to smilestarting a small businessdatabase migration riskhow should I think about learning biology?The expected output is JSON with a questions array.
python
import jsonimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizer repo_id = "aungkomyint/tara1.2-quest" tokenizer = AutoTokenizer.from_pretrained(repo_id)model = AutoModelForCausalLM.from_pretrained(repo_id)model.eval() def generate_questions(user_text): prompt = f"User: {user_text.strip()}\nAssistant:\n" inputs = tokenizer(prompt, return_tensors="pt") inputs.pop("token_type_ids", None) with torch.no_grad(): output = model.generate( **inputs, max_new_tokens=160, do_sample=True, temperature=0.7, top_p=0.9, repetition_penalty=1.18, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, ) text = tokenizer.decode(output[0], skip_special_tokens=True) reply = text.split("Assistant:", 1)[-1].strip() return reply reply = generate_questions("cooking")print(reply) try: data = json.loads(reply) print("question count:", len(data.get("questions", [])))except json.JSONDecodeError: print("Model did not return valid JSON for this sample.")Prompt:
text
User: cookingAssistant:Sample output:
json
{ "questions": [ "What constraints around time, money, energy, or rules shape cooking?", "Which part of cooking is most uncertain right now?", "How would a beginner and an expert frame cooking differently?", "Why does cooking matter for the larger goal?", "What evidence would make your thinking about cooking more reliable?" ]}Outputs are stochastic when sampling is enabled. Validate JSON in your application.
This checkpoint was trained as a small SFT experiment on top of Tara 1.2 base model work. The goal was to test whether a very small causal language model could learn a narrow JSON question-generation interface.
The training data focused on:
Local benchmark results showed:
This release is therefore best treated as an educational/research checkpoint, not a production assistant.
Good starting settings:
text
temperature=0.7top_p=0.9repetition_penalty=1.18max_new_tokens=160For stricter output, use greedy decoding, but expect more repetition.
tara1.2-quest is a successor experiment to aungkomyint/tara1.1. Tara 1.1 was a broader tiny assistant experiment. Tara 1.2 Quest narrows the behavior to JSON question generation.
If you use this model, cite it as:
text
Aung Ko Myint. Tara 1.2 Quest. 2026. Hugging Face model checkpoint.import jsonimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizer repo_id = "aungkomyint/tara1.2-quest" tokenizer = AutoTokenizer.from_pretrained(repo_id)model = AutoModelForCausalLM.from_pretrained(repo_id)model.eval() def generate_questions(user_text): prompt = f"User: {user_text.strip()}\nAssistant:\n" inputs = tokenizer(prompt, return_tensors="pt") inputs.pop("token_type_ids", None) with torch.no_grad(): output = model.generate( **inputs, max_new_tokens=160, do_sample=True, temperature=0.7, top_p=0.9, repetition_penalty=1.18, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, ) text = tokenizer.decode(output[0], skip_special_tokens=True) reply = text.split("Assistant:", 1)[-1].strip() return reply reply = generate_questions("cooking")print(reply) try: data = json.loads(reply) print("question count:", len(data.get("questions", [])))except json.JSONDecodeError: print("Model did not return valid JSON for this sample."){ "questions": [ "What constraints around time, money, energy, or rules shape cooking?", "Which part of cooking is most uncertain right now?", "How would a beginner and an expert frame cooking differently?", "Why does cooking matter for the larger goal?", "What evidence would make your thinking about cooking more reliable?" ]}temperature=0.7top_p=0.9repetition_penalty=1.18max_new_tokens=160