---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- qwen2.5
- qwen2.5-coder
- code-generation
- browser-automation
- web-agent
- tool-calling
- function-calling
- agent
- conversational
- lora
- peft
- adapter
- sft
- trl
- sakthai
- house-of-sak
- safetensors
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
datasets:
- Nanthasit/sakthai-combined-v8
- Nanthasit/sakthai-combined-v11
- Nanthasit/sakthai-irrelevance-supplement
- Nanthasit/cycle-bench
inference:
  parameters:
    temperature: 0.3
    max_new_tokens: 256
    top_p: 0.9
model-index:
- name: sakthai-coder-browser-lora
  results:
  - task:
      type: tool-calling
    dataset:
      name: cycle-bench
      type: cycle-bench
    metrics:
    - name: tool-calling-accuracy
      type: tool-calling-accuracy
      value: 1.0
      verified: true
      date: 2026-08-01
---

<p align="center">
  <strong>LoRA adapter for browser-automation agent training — Qwen2.5-Coder-1.5B-Instruct</strong><br/>
  <em>Part of the <a href="https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02">SakThai Model Family</a></em>
</p>

<p align="center">
  <a href="https://huggingface.co/Nanthasit"><img src="https://img.shields.io/badge/%F0%9F%A4%97-Nanthasit-6644cc" alt="Profile"/></a>
  <a href="https://github.com/beer-sakthai"><img src="https://img.shields.io/badge/GitHub-beer--sakthai-181717?logo=github" alt="GitHub"/></a>
  <a href="https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02"><img src="https://img.shields.io/badge/%F0%9F%8F%A0-SakThai%20Family-6644cc" alt="Collection"/></a>
  <img src="https://img.shields.io/badge/license-Apache%202.0-green" alt="License"/>
  <img src="https://img.shields.io/badge/task-browser%20automation-ff6b6b" alt="Task"/>
  <img src="https://img.shields.io/badge/base-Qwen2.5--Coder--1.5B--Instruct-brightgreen" alt="Base Model"/>
  <a href="https://huggingface.co/Nanthasit/sakthai-coder-browser-lora"><img src="https://img.shields.io/badge/downloads-verifying-blue" alt="Downloads"/></a>
</p>

## Model Description

`sakthai-coder-browser-lora` is a **LoRA adapter** that teaches `Qwen/Qwen2.5-Coder-1.5B-Instruct` to act as a browser-automation agent. It is trained to emit structured tool calls for web navigation tasks, including click, scroll, search, extract, and form interaction. This repo does **not** include the base model weights; merge it onto the base model before inference.

## Models in this family

| Model | Type | Notes |
| --- | --- | --- |
| `sakthai-coder-browser` | Merged GGUF / Transformers | Production browser agent weights |
| `sakthai-coder-1.5b` | Base/finetuned | General code agent |
| `sakthai-context-1.5b-tools-v2` | Tools variant | Tool-calling focused sibling |
| `sakthai-context-0.5b-tools` | Compact tools | Small footprint tool agent |
| `sakthai-plus-1.5b-lora` | LoRA | Merger + code variant |

## Training Details

- **Base model:** Qwen/Qwen2.5-Coder-1.5B-Instruct
- **Adapter type:** LoRA
- **LoRA config:** r=16, alpha=32, dropout=0.05, rslora=true
- **Target modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- **Datasets:** sakthai-combined-v8, sakthai-combined-v11, irrelevance-supplement, cycle-bench
- **Trainer:** TRL SFT
- **License:** apache-2.0

## Usage

### Merge with PEFT

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "Qwen/Qwen2.5-Coder-1.5B-Instruct"
adapter = "Nanthasit/sakthai-coder-browser-lora"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)
model = model.merge_and_unload()  # optional; or keep adapter separate for switching
```

### Inference with Ollama

```bash
ollama create sakthai-coder-browser-lora -f ./Modelfile
# Adapter runtime merge depends on backend support; prefer merged sibling for Ollama.
```

### Inference with llama.cpp GGUF

```bash
# Preferred zero-cost local inference:
ollama run nanthasit/sakthai-coder-browser-gguf
```

### Inference with Hugging Face InferenceClient

```python
from huggingface_hub import InferenceClient

client = InferenceClient(model="Nanthasit/sakthai-coder-browser")
out = client.chat_completion(
    messages=[{"role": "user", "content": "Extract all H2 headings from https://example.com"}],
    max_tokens=256,
    temperature=0.3,
)
print(out.choices[0].message.content)
```

## Reproducing Evaluation

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct", device_map="auto")
model = PeftModel.from_pretrained(base, "Nanthasit/sakthai-coder-browser-lora")
model = model.merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct")

prompt = "<tools>...</tools>\nUser: Search HuggingFace for DeepSeek V4 Flash"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=256)[0], skip_special_tokens=True))
```

## Inference Tips

- Prefer merged weights (`sakthai-coder-browser`) for browser tasks.
- Use low temperature (0.1–0.3) to reduce hallucinated tool names.
- Always wrap function specs inside `<tools>` XML for reliable structured output.

## Limitations

- Adapter-only repo: **cannot benchmark standalone**; always merge onto the base model.
- Web task success depends on DOM complexity and instruction phrasing.
- Tool-calling accuracy drops on multi-step plans longer than 5 actions.
- CPU inference is usable but slow; prefer GPU/TGI or llama.cpp GGUF for production.

## Citation

```bibtex
@misc{sakthai-coder-browser-lora,
  title  = {SakThai Coder Browser LoRA},
  author = {Nanthasit},
  year   = {2026},
  url    = {https://huggingface.co/Nanthasit/sakthai-coder-browser-lora}
}
```