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Nanthasit
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Nanthasit/sakthai-context-0.5b-tools
Input Modalities
Output Modalities
Supported Functionality
An experimental SFT bootstrap of sakthai-context-0.5b-tools,
built to test whether the model's agentic (multi-turn) tool-use could be improved. This is an honest,
mixed-result research checkpoint — read the results before treating it as a replacement for the base model.
This adapter is a LoRA (r=16) supervised-fine-tune of sakthai-context-0.5b-tools on 6 verified oracle
trajectories — one correct multi-step solution (read → edit → submit) for each task in the
hermes-tool-use-rl-env
agentic coding environment. Each trajectory was checked to earn full reward against the environment's real
grader before training.
The base 0.5b-tools solves those agentic tasks 0/6, which means reinforcement learning (GRPO) has no
successes to reinforce from the start. This SFT was an attempt to lift the success rate above zero so RL
could take over.
| Benchmark | Base 0.5b-tools | This checkpoint |
|---|---|---|
| Agentic (hermes-env, 6 tasks, native template) | 0/6 | 1/6 ✅ |
| Single-shot selection (sakthai-bench-v2) | 91.0% | 77.8% ⚠️ |
| Single-shot arguments (sakthai-bench-v2) | 45.7% | 36.7% ⚠️ |
The SFT nudged agentic in the right direction (+1 task solved from scratch) but the config was too aggressive: it cost ~13 points of single-shot selection for that one agentic task. It also did not provide enough of a lift for a follow-up GRPO run to gain traction (GRPO from this checkpoint still saw zero reward variance).
Bottom line: this is a proof that the SFT-bootstrap direction works, not a recommended replacement for the
base model. If you want the strong single-shot tool selector, use sakthai-context-0.5b-tools.
The agentic improvement effort for this family is more promising on the 7B model (which already solves 3/6 and
has a real RL learning signal).
Important format note: this adapter was trained with the model's native chat template (
tokenizer.apply_chat_template(messages, tools=...)). Evaluate/serve it in that format — not the hand-rolled<tools>renderer some sibling models use — or it will underperform.
sakthai-context-0.5b-toolshermes-tool-use-rl-envsakthai-bench-v2/FINDINGS_20260731.mdThis adapter is a LoRA on top of sakthai-context-0.5b-tools. Load it with the base
model and use the native chat template (tokenizer.apply_chat_template(messages, tools=...)). Do NOT force it through a hand-rolled <tools> renderer or it will underperform.
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase_id = "Nanthasit/sakthai-context-0.5b-tools"adapter_id = "Nanthasit/sakthai-context-0.5b-tools-sft"tokenizer = AutoTokenizer.from_pretrained(base_id)base_model = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")model = PeftModel.from_pretrained(base_model, adapter_id)model.eval()tools = [{"type": "function","function": {"name": "list_files","description": "List files in a directory.","parameters": {"type": "object","properties": {"path": {"type": "string", "description": "Directory path"}},"required": ["path"]}}}]messages = [{"role": "user", "content": "List files under /tmp."}]prompt = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False)inputs = tokenizer(prompt, return_tensors="pt").to(base_model.device)out = model.generate(**inputs, max_new_tokens=256)print(tokenizer.decode(out[0], skip_special_tokens=True))
If you only need the adapter weights, load them with PeftModel on top of the base model.
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase_id = "Nanthasit/sakthai-context-0.5b-tools"adapter_id = "Nanthasit/sakthai-context-0.5b-tools-sft"tokenizer = AutoTokenizer.from_pretrained(base_id)base_model = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")model = PeftModel.from_pretrained(base_model, adapter_id)model.eval()prompt = "List the tools available for this session."inputs = tokenizer(prompt, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=256)print(tokenizer.decode(out[0], skip_special_tokens=True))
tools=....bibtex
@misc{sakthai2026context05btoolssft,title={SakThai Context 0.5B Tools SFT: Experimental Agentic Bootstrap},author={SakThai Family (beer-sakthai)},year={2026},publisher={Hugging Face},howpublished={\url{https://huggingface.co/Nanthasit/sakthai-context-0.5b-tools-sft}}
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