aungkomyint/tara1.3

Tara 1.3

Tara 1.3 is a tiny experimental AI-engineering tool-call model. It is trained to read a short User: prompt and emit a compact JSON object that either selects a tool or answers with tool: "none".

This release is best treated as a research checkpoint for structured tool-routing experiments, not as a production assistant.

Model Details

• Model name: tara1.3
• Internal checkpoint: tara-1.3-ai-engineer-toolcall-sft-v4-plaintok-from300/checkpoint-100
• Architecture: LlamaForCausalLM
• Context length: 1,024 tokens
• Vocabulary size: 16,384
• Hidden size: 512
• Layers: 7
• Attention heads: 8
• Weights format: safetensors
• License: Apache-2.0

Capability

Tara 1.3 is designed to produce JSON-style tool calls for a small AI-engineering tool set.

Supported tool names:

• weather
• search
• segment
• evaluate_model
• train_sft
• inspect_file
• extract_json
• none

Example target shapes:

{"tool":"weather","location":"Bangkok tomorrow"}

{"tool":"search","query":"Python list comprehension examples"}

{"tool":"train_sft","base_model":"models/base","dataset":"data/train.txt","output_dir":"outputs/run"}

{"tool":"none","response":"Tokenizer validation passed. Next, run a small generation smoke test."}

Quick Start

import json
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "aungkomyint/tara1.3"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id)
model.eval()

def generate_tool_call(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=120,
            do_sample=False,
            repetition_penalty=1.08,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id,
        )

    text = tokenizer.decode(output[0], skip_special_tokens=False)
    reply = text[len(prompt):] if text.startswith(prompt) else text.split("Assistant:", 1)[-1]
    reply = reply.split("<|endoftext|>", 1)[0].split("<|pad|>", 1)[0].strip()
    return reply

reply = generate_tool_call("Search the web for Python list comprehension examples.")
print(reply)

try:
    parsed = json.loads(reply)
    print("tool:", parsed.get("tool"))
except json.JSONDecodeError:
    print("Model did not return valid JSON for this sample.")

Recommended Prompt Format

Use a simple instruction format:

User: <request>
Assistant:

Greedy decoding is recommended for tool-call tests:

do_sample=False
max_new_tokens=120
repetition_penalty=1.08

Training Summary

Tara 1.3 was trained as a supervised fine-tuning continuation for AI-engineering tool calls.

Training configuration:

• Steps: 300
• Block size: 1,024
• Batch size: 8
• Gradient accumulation: 4
• Effective batch size: 32
• Learning rate: 5e-5
• Warmup steps: 15
• Weight decay: 0.01
• Loss mask: only the final Assistant response is trained; earlier turns are context

Dataset:

• Train examples: 8,945
• Eval examples: 777
• Mixture: no-tool/chat behavior plus capped tool-call examples

Local Evaluation

The released checkpoint was selected from a small local comparison on 2026-06-24.

The 10-prompt eval covered weather, search, segmentation, model evaluation, SFT training, file inspection, JSON extraction, and no-tool/general responses.

Checkpoint	Valid JSON	Schema OK	Expected Tool Match
checkpoint-100	6/10	5/10	5/10
checkpoint-200	5/10	5/10	5/10
checkpoint-300	5/10	5/10	5/10

checkpoint-100 was selected because it tied the other continued checkpoints on schema correctness and tool selection while producing one more valid JSON output.

Tokenizer validation passed: tool-call JSON tokenizes through the plain BPE vocabulary without old chat/tool special tokens.

Limitations

• This is a very small experimental model.
• It can emit malformed JSON.
• It can choose the right tool but fill arguments with copied or unrelated values.
• General tool: "none" responses are unstable.
• It is not reliable for autonomous tool execution without validation, repair, and fallback logic.
• It should not be used for medical, legal, financial, safety, or other high-stakes decisions.

Suggested Runtime Guardrails

Applications should:

• Parse the output with a JSON parser.
• Validate the tool name against an allowlist.
• Validate required fields for each tool.
• Reject or repair malformed JSON.
• Require user confirmation before destructive or external actions.

Citation

If you use this model, cite it as:

Aung Ko Myint. Tara 1.3. 2026. Hugging Face model checkpoint.