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Nanthasit
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Model Details
Model Provider
Nanthasit
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Attribute | Value |
|---|---|
| Base model | Qwen/Qwen2.5-1.5B-Instruct |
| Parameters | 1.5B |
| Adapter | LoRA (r=16, alpha=32, dropout=0.05) |
| LoRA target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| LoRA variant | rslora |
| Context length | 32,768 tokens |
| Method | SFT via TRL |
| License | Apache 2.0 |
| Output | Text generation |
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase_id = "Qwen/Qwen2.5-1.5B-Instruct"lora_id = "Nanthasit/sakthai-plus-1.5b-lora"base_model = AutoModelForCausalLM.from_pretrained(base_id)tokenizer = AutoTokenizer.from_pretrained(base_id)model = PeftModel.from_pretrained(base_model, lora_id)model = model.merge_and_unload()inputs = tokenizer("You are a helpful assistant.", return_tensors="pt")outputs = model.generate(**inputs, max_new_tokens=256)print(tokenizer.decode(outputs[0], skip_special_tokens=True))
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "Nanthasit/sakthai-plus-1.5b"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")prompt = "Plan a 3-day trip to Lisbon with a €200 budget."inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)print(tokenizer.decode(outputs[0], skip_special_tokens=True))
bash
curl -X POST https://api-inference.huggingface.co/models/Nanthasit/sakthai-plus-1.5b \-H "Authorization: Bearer ***" \-H "Content-Type: application/json" \-d '{"inputs": "Your prompt here"}'
| Benchmark | Value | Status |
|---|---|---|
| Tool Calling — send_email (SakThai Bench v2) | 1.0 | verified |
| General Assistant — text generation | pending | multi-trial eval ongoing |
Evidence files: .eval_results/cron-eval-2026-07-31-2.yaml.
See the leaderboard Space for live updates as scores are verified.
Training Data: SakThai Combined v7 (2,003 examples — tool-use, Q&A, multi-turn conversation, reasoning tasks)
| Item | Value |
|---|---|
| Base | Qwen/Qwen2.5-1.5B-Instruct |
| Dataset | Nanthasit/sakthai-combined-v7 |
| Framework | TRL + PEFT 0.19.1 |
| Adapter type | LoRA + rslora |
| LoRA rank | 16 |
| LoRA alpha | 32 |
| Dropout | 0.05 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Task type |
bash
python scripts/eval_tool_calling.py \--model_id Nanthasit/sakthai-plus-1.5b-lora \--base_model_id Qwen/Qwen2.5-1.5B-Instruct \--dataset Nanthasit/sakthai-bench-v3 \--task_type tool_calling \--trials 3
bash
python scripts/train_lora.py \--base_model Qwen/Qwen2.5-1.5B-Instruct \--dataset Nanthasit/sakthai-combined-v7 \--output_dir lora-outpython scripts/merge_lora.py \--base_model Qwen/Qwen2.5-1.5B-Instruct \--adapter_dir lora-out \--output_dir merged
bibtex
@misc{sakthai-plus-1.5b-lora,title = {SakThai Plus 1.5B LoRA},author = {Beer Nanthasit and SakThai Agents},year = {2026},url = {https://huggingface.co/Nanthasit/sakthai-plus-1.5b-lora}
| Asset | Link |
|---|---|
| Collection | SakThai Model Family |
| Leaderboard | Benchmark Results |
| Training Dataset | SakThai Combined v7 |
| Base Model | Qwen2.5-1.5B-Instruct |
| Merged Version |
Apache License 2.0. Derived from Qwen/Qwen2.5-1.5B-Instruct (Apache 2.0).
SakThai Agents always tell you: this model exists because someone chose to build with no money and infinite heart. Use it well. 💜
| CAUSAL_LM |
| Dtype | bfloat16 |
| Context | 32,768 tokens |
| Repository | beer-sakthai/Sak-Family-Agent |