Instructions to use Nanthasit/sakthai-coder-browser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nanthasit/sakthai-coder-browser with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Nanthasit/sakthai-coder-browser")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-coder-browser")
model = AutoModelForCausalLM.from_pretrained("Nanthasit/sakthai-coder-browser", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nanthasit/sakthai-coder-browser with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nanthasit/sakthai-coder-browser"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-coder-browser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanthasit/sakthai-coder-browser

SGLang

How to use Nanthasit/sakthai-coder-browser with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Nanthasit/sakthai-coder-browser" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-coder-browser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Nanthasit/sakthai-coder-browser" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-coder-browser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nanthasit/sakthai-coder-browser with Docker Model Runner:
```
docker model run hf.co/Nanthasit/sakthai-coder-browser
```

sakthai-coder-browser / README.md

Nanthasit

docs: fix malformed dataset tag in YAML frontmatter

eeb26d1 verified about 4 hours ago

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	---
	license: apache-2.0
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- qwen2.5
	- qwen2.5-coder
	- sakthai
	- house-of-sak
	- browser-automation
	- web-agent
	- tool-calling
	- function-calling
	- tool-use
	- agent
	- code-generation
	- finetuned
	- finetune
	- sft
	- text-generation
	- merged
	- conversational
	- safetensors
	- transformers
	base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
	datasets:
	- Nanthasit/sakthai-combined-v8
	- 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
	widget:
	- text: "Search for the latest AI news and summarize the top story."
	example_title: "Navigate + extract"
	- text: "Go to Hacker News, find the top post, and click through to read it."
	example_title: "Multi-step navigation"
	- text: "Open google.com, search for 'weather in Cork Ireland', and tell me the current conditions."
	example_title: "Search + extract weather"
	model-index:
	- name: SakThai Coder Browser
	results:
	- task:
	type: text-generation
	name: Browser Tool Calling (diagnostic)
	dataset:
	name: sakthai-coder-browser internal probe 2026-07-31
	type: internal
	metrics:
	- type: tool_call_rate
	value: 0.0
	verified: true
	notes: Multi-trial llama.cpp GGUF Q4_K_M CPU probe, 2026-07-31 05:21 UTC. 3/3 trials returned 0 output tokens; diagnosed as corrupted merged weights (nonzero attention-projection biases). Not deployable until clean re-merge and re-verification.
	- type: valid_json_rate
	value: 0.0
	verified: true
	notes: No <tool_call> JSON emitted in any trial at temp <= 0.7.
	---

	# SakThai Coder Browser

	<p align="center">
	<strong>Browser automation agent — Qwen2.5-Coder-1.5B-Instruct fine-tuned for web interaction</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/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fmodels%2FNanthasit%2Fsakthai-coder-browser&query=%24.downloads&label=downloads&color=blue" alt="Downloads"/>
	<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-blueviolet" alt="Base"/>
	</p>

	> [!CAUTION]
	> BROKEN — DO NOT DEPLOY (as of 2026-07-31) — The merged weights in this repo are corrupted by a faulty LoRA merge: all 84 attention-projection bias tensors are non-zero while Qwen2 initializes these biases to ZERO (layer-0 `k_proj.bias` absmean 27.7 / max 354). Multi-trial inference probes produced only whitespace loops — 0 tool calls, 0 valid JSON at temp <= 0.7. Full evidence: [`.eval_results/benchmark-20260731_052122.yaml`](https://huggingface.co/Nanthasit/sakthai-coder-browser/blob/main/.eval_results/benchmark-20260731_052122.yaml). The fault is in the weights, not the GGUF conversion or the prompt format. The [GGUF variant](https://huggingface.co/Nanthasit/sakthai-coder-browser-gguf) was converted from these same corrupted weights and must be re-checked; the [LoRA adapter](https://huggingface.co/Nanthasit/sakthai-coder-browser-lora) needs a clean re-merge. Treat this repo as not deployable until re-merged and re-verified.

	---

	## Model Description

	SakThai Coder Browser transforms Qwen2.5-Coder-1.5B-Instruct into a browser automation assistant that outputs structured `<tool_call>` XML/JSON for web interaction. It can navigate pages, click elements, type text, and extract content — designed to work with browser automation frameworks.

	Available actions via `<tool_call>` XML:

	\| Tool \| Example \|
	\|------\|---------\|
	\| `browser_navigate(url)` \| `<tool_call>{"name": "browser_navigate", "arguments": {"url": "https://example.com"}}</tool_call>` \|
	\| `browser_click(element)` \| `<tool_call>{"name": "browser_click", "arguments": {"element": "#search-button"}}</tool_call>` \|
	\| `browser_type(element, text)` \| `<tool_call>{"name": "browser_type", "arguments": {"element": "#search-input", "text": "AI news"}}</tool_call>` \|
	\| `browser_extract()` \| `<tool_call>{"name": "browser_extract", "arguments": {}}</tool_call>` \|

	---

	## Tool-Calling Format

	The repo ships its own `chat_template.jinja` (Qwen2.5 tool-calling style). When tools are provided, the system prompt embeds function signatures inside `<tools></tools>` XML tags and the model replies with a `<tool_call>` JSON block:

	```text
	<\|im_start\|>system
	You are Qwen, created by Alibaba Cloud. You are a helpful assistant.

	# Tools

	You may call one or more functions to assist with the user query.

	You are provided with function signatures within <tools></tools> XML tags:
	<tools>
	{"type": "function", "function": {"name": "browser_navigate", "parameters": {...}}}
	</tools>

	For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
	<tool_call>
	{"name": <function-name>, "arguments": <args-json-object>}
	</tool_call><\|im_end\|>
	<\|im_start\|>user
	Search for the latest AI news.<\|im_end\|>
	<\|im_start\|>assistant
	<tool_call>
	{"name": "browser_navigate", "arguments": {"url": "https://news.google.com"}}
	</tool_call><\|im_end\|>
	```

	Tool results are wrapped in `<tool_response></tool_response>` blocks. Multi-turn loops are supported by the chat template.

	---

	## Quick Start

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model = AutoModelForCausalLM.from_pretrained(
	"Nanthasit/sakthai-coder-browser",
	torch_dtype=torch.bfloat16,
	device_map="auto"
	)
	tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-coder-browser")

	messages = [
	{"role": "system", "content": "You are SakThai Browser Agent. Use <tool_call> blocks to control the browser."},
	{"role": "user", "content": "Search for the latest AI news and summarize the top story."},
	]
	inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
	outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.3)
	print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
	```

	Expected output format:
	```
	<tool_call>{"name": "browser_navigate", "arguments": {"url": "https://news.google.com"}}</tool_call>
	```

	> Use the chat template. This model was trained with the Qwen2.5 tool-calling format — pass tools through `apply_chat_template` (or the repo's `chat_template.jinja`) rather than hand-rolling prompts.

	### GGUF / llama.cpp variant

	Prefer CPU inference or Ollama? Use the [GGUF build](https://huggingface.co/Nanthasit/sakthai-coder-browser-gguf) (F16, ~7.1 GB) with llama.cpp:

	```bash
	huggingface-cli download Nanthasit/sakthai-coder-browser-gguf \
	sakthai-coder-browser-f16.gguf --local-dir ./
	./llama-cli -m sakthai-coder-browser-f16.gguf \
	-p "<\|im_start\|>system\nYou are a browser automation assistant.<\|im_end\|>\n<\|im_start\|>user\nGo to google.com and search for the latest AI news<\|im_end\|>\n<\|im_start\|>assistant\n" \
	-n 512 -t 8 --temp 0.3
	```

	---

	## Architecture

	Verified from this repo's `config.json` (transformers 5.14.1):

	\| Property \| Value \|
	\|----------\|-------\|
	\| Base Model \| [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) \|
	\| Architecture \| Qwen2ForCausalLM (decoder-only transformer) \|
	\| Parameters \| 1,543,714,304 (1.54B) \|
	\| Hidden Size \| 1,536 \|
	\| Layers \| 28 \|
	\| Attention Heads \| 12 (GQA, 2 KV heads) \|
	\| Intermediate Size \| 8,960 \|
	\| Max Position \| 32,768 tokens \|
	\| Vocab Size \| 151,936 \|
	\| RoPE Theta \| 1,000,000 \|
	\| Activation \| SiLU (SwiGLU) \|
	\| Normalization \| RMSNorm (eps=1e-6) \|
	\| Precision \| BF16 \|
	\| Weights \| Single `model.safetensors` — 3,087,467,144 B (2.88 GB, API-verified) \|
	\| Tied embeddings \| yes (`tie_word_embeddings: true`) \|

	---

	## Training Details

	\| Detail \| Value \|
	\|--------\|-------\|
	\| Base model \| Qwen/Qwen2.5-Coder-1.5B-Instruct \|
	\| Method \| SFT via LoRA (r=16, alpha=32, dropout 0.05, rsLoRA) on all 7 linear projections, then merged to full weights \|
	\| Context length \| 32,768 tokens \|
	\| Precision \| BF16 \|
	\| Hardware \| Free T4 GPU (Kaggle / Colab) \|
	\| Budget \| $0 \|

	Training configuration mirrors the sibling [sakthai-coder-browser-lora](https://huggingface.co/Nanthasit/sakthai-coder-browser-lora) adapter (verified from its `adapter_config.json`: `peft` 0.20.0, `use_rslora: true`, `lora_dropout: 0.05`, target modules q/k/v/o/gate/up/down_proj).

	---

	## Evaluation & Status

	Honest status: inference benchmarks were attempted and did not produce output. The repo's own `.eval_results/benchmark-20260731_052122.yaml` records a llama.cpp GGUF Q4_K_M run (3 trials, CPU, 2 threads, 2026-07-31 05:21 UTC, tool-calling browser prompt, 244 input tokens) in which all 3 trials returned 0 output tokens — no tool call, no valid JSON, no correct answer:

	\| Trial \| Seed \| Output tokens \| Tool call \| Valid JSON \| Correct answer \|
	\|:-----:\|:----:\|:-------------:\|:---------:\|:----------:\|:--------------:\|
	\| 1 \| 7 \| 0 \| No \| No \| No \|
	\| 2 \| 42 \| 0 \| No \| No \| No \|
	\| 3 \| 1337 \| 0 \| No \| No \| No \|

	Verdict — MODEL_BROKEN (bias corruption): the repo's own eval YAML (updated 2026-07-31 05:50 UTC) includes weight inspection of `model.safetensors` that proves the fault is in the weights, not the harness:

	- Qwen2 initializes attention-projection biases to zero; this merge left all 84 bias tensors non-zero (absmean > 0.01), e.g. layer-0 `k_proj.bias` absmean 27.7 / max 354, layer-0 `q_proj.bias` absmean 1.17
	- Degenerate generation at temp <= 0.7 on all 3 seeds — whitespace loops (150 newline tokens, 0 tool calls, 0 valid JSON); only at temp 1.5 did the model emit `Hi` on a trivial prompt
	- GGUF tensor layout is structurally identical to the working `sakthai-plus-1.5b` GGUF (338 tensors, same names) -> the fault is in the merged weights, not the conversion
	- No NaN present; `embed_tokens` is normal (absmean 0.0136) — corruption is isolated to the attention biases

	Recommended fix: re-merge the LoRA adapter into `Qwen2.5-Coder-1.5B-Instruct` with correct bias handling (do not write adapter-state biases into the base where Qwen2 expects zeros), re-run the multi-trial probe, and update this card. Until then, this repo is not deployable.

	Hosted inference: not available — router probe returned 404 (`Not Found`) and the legacy api-inference host does not resolve (per the same eval YAML). No `model-index` is published because there are no verified scores yet; publishing one would be misleading.

	Ecosystem status from `.eval_results/cron-eval-sakthai-coder-browser-2026-07-30-1.yaml`: card quality 85/100, repo hygiene 95/100, health 23/100 (rank 20/20 — new repo, zero downloads at eval time; popularity/momentum/benchmarks components are 0 because the repo had no traction yet).

	---

	## Repo Contents

	\| File \| Size \| Purpose \|
	\|------\|-----:\|---------\|
	\| `model.safetensors` \| 3,087,467,144 B \| Merged BF16 weights (single shard) \|
	\| `chat_template.jinja` \| 2,507 B \| Qwen2.5 tool-calling chat template \|
	\| `config.json` \| 1,373 B \| Qwen2 config (32K ctx, GQA 2 KV heads) \|
	\| `tokenizer.json` \| 11,421,892 B \| Tokenizer \|
	\| `.eval_results/` \| — \| benchmark + cron-eval YAMLs \|

	---

	## Sibling Models

	\| Variant \| Repository \|
	\|:--------\|:-----------\|
	\| LoRA Adapter (unmerged) \| [sakthai-coder-browser-lora](https://huggingface.co/Nanthasit/sakthai-coder-browser-lora) \|
	\| GGUF (llama.cpp / Ollama) \| [sakthai-coder-browser-gguf](https://huggingface.co/Nanthasit/sakthai-coder-browser-gguf) \|
	\| Merged model (this repo) \| [sakthai-coder-browser](https://huggingface.co/Nanthasit/sakthai-coder-browser) \|

	---

	## SakThai Model Family

	One of 25 public model repos in the [SakThai Model Family collection](https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02) (plus companion repos [sakthai-bench-v3](https://huggingface.co/Nanthasit/sakthai-bench-v3), [sakthai-pipeline](https://huggingface.co/Nanthasit/sakthai-pipeline), [eval_results](https://huggingface.co/Nanthasit/eval_results), [sft-out](https://huggingface.co/Nanthasit/sft-out), and adapter pilots). Live download counts as of 2026-08-01; this repo has 54 downloads.

	\| Model \| Downloads \|
	\|:------\|----------:\|
	\| [sakthai-context-1.5b-merged](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged) \| 1,855 \|
	\| [sakthai-context-0.5b-merged](https://huggingface.co/Nanthasit/sakthai-context-0.5b-merged) \| 1,692 \|
	\| [sakthai-context-7b-merged](https://huggingface.co/Nanthasit/sakthai-context-7b-merged) \| 1,024 \|
	\| [sakthai-embedding-multilingual](https://huggingface.co/Nanthasit/sakthai-embedding-multilingual) \| 627 \|
	\| [sakthai-context-7b-128k](https://huggingface.co/Nanthasit/sakthai-context-7b-128k) \| 610 \|
	\| [sakthai-context-7b-tools](https://huggingface.co/Nanthasit/sakthai-context-7b-tools) \| 489 \|
	\| [sakthai-context-1.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools) \| 477 \|
	\| [sakthai-context-1.5b-merged-v2](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged-v2) \| 337 \|
	\| [sakthai-vision-7b](https://huggingface.co/Nanthasit/sakthai-vision-7b) \| 315 \|
	\| [sakthai-plus-1.5b-lora](https://huggingface.co/Nanthasit/sakthai-plus-1.5b-lora) \| 306 \|
	\| [sakthai-context-0.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-0.5b-tools) \| 251 \|
	\| [sakthai-tts-model](https://huggingface.co/Nanthasit/sakthai-tts-model) \| 248 \|
	\| [sakthai-plus-1.5b](https://huggingface.co/Nanthasit/sakthai-plus-1.5b) \| 244 \|
	\| [sakthai-context-1.5b-tools-v2](https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools-v2) \| 173 \|
	\| [sakthai-coder-1.5b](https://huggingface.co/Nanthasit/sakthai-coder-1.5b) \| 151 \|
	\| Coder Browser (this model) ⬅ \| 54 \|
	\| [sakthai-coder-browser-gguf](https://huggingface.co/Nanthasit/sakthai-coder-browser-gguf) \| 35 \|
	\| [sakthai-embedding](https://huggingface.co/Nanthasit/sakthai-embedding) \| 23 \|
	\| [sakthai-coder-browser-lora](https://huggingface.co/Nanthasit/sakthai-coder-browser-lora) \| 21 \|
	\| [sakthai-plus-1.5b-coder](https://huggingface.co/Nanthasit/sakthai-plus-1.5b-coder) \| 0 \|
	\| [eval_results](https://huggingface.co/Nanthasit/eval_results) \| 0 \|
	\| [sakthai-bench-v3](https://huggingface.co/Nanthasit/sakthai-bench-v3) \| 0 \|
	\| [sakthai-pipeline](https://huggingface.co/Nanthasit/sakthai-pipeline) \| 0 \|
	\| [sakthai-context-0.5b-tools-sft](https://huggingface.co/Nanthasit/sakthai-context-0.5b-tools-sft) \| 0 \|
	\| [sft-out](https://huggingface.co/Nanthasit/sft-out) \| 0 \|
	\| [sakthai-context-0.5b-tools-sft-v2](https://huggingface.co/Nanthasit/sakthai-context-0.5b-tools-sft-v2) \| 0 \|

	---

	## Limitations

	- BROKEN weights — all 84 attention bias tensors are corrupted by a faulty LoRA merge (see [Evaluation & Status](#evaluation--status)); do not deploy until re-merged and re-verified
	- No verified benchmark scores yet — `model-index` currently carries 0% `tool_call_rate` and 0% `valid_json_rate` from the 2026-07-31 diagnostic probe; these are failure signals from corrupted weights, not representative task scores
	- Text-only — cannot see images or screenshots (use [sakthai-vision-7b](https://huggingface.co/Nanthasit/sakthai-vision-7b) for vision tasks)
	- English-only web actions — training data is primarily English web interactions; non-English pages may yield lower-quality actions
	- Context-limited — best results with page content <= 4K tokens per interaction; long pages can exceed the model's effective working memory
	- Not servable on HF serverless inference — no provider supports this custom fine-tune (router 404 verified); run locally via Transformers or the GGUF build once weights are repaired

	---

	## Citation

	If you use SakThai Coder Browser in your work, please cite the base model and the fine-tuning approach:

	```bibtex
	@misc{qwen25coder,
	title = {Qwen2.5-Coder: Code Language Models},
	author = {Qwen Team},
	year = {2024},
	publisher = {Hugging Face},
	howpublished = {\url{https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct}}
	}

	@misc{sakthai-model-family,
	title = {SakThai Model Family: Zero-Budget Fine-Tuned Language Models},
	author = {{Beer Nanthasit}},
	year = {2026},
	publisher = {Hugging Face},
	howpublished = {\url{https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02}}
	}
	```

	---

	Part of the [SakThai Model Family](https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02). Built with love, tears, and zero budget. From a shelter in Cork, Ireland, to the world.