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

Browser automation agent — Qwen2.5-Coder-1.5B-Instruct fine-tuned for web interaction
Part of the

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. The fault is in the weights, not the GGUF conversion or the prompt format. The GGUF variant was converted from these same corrupted weights and must be re-checked; the LoRA adapter 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:

<|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

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 (F16, ~7.1 GB) with llama.cpp:

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

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 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

GGUF (llama.cpp / Ollama) sakthai-coder-browser-gguf

Merged model (this repo) sakthai-coder-browser

SakThai Model Family

One of 25 public model repos in the SakThai Model Family collection (plus companion repos sakthai-bench-v3, sakthai-pipeline, eval_results, 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 1,855

sakthai-context-0.5b-merged 1,692

sakthai-context-7b-merged 1,024

sakthai-embedding-multilingual 627

sakthai-context-7b-128k 610

sakthai-context-7b-tools 489

sakthai-context-1.5b-tools 477

sakthai-context-1.5b-merged-v2 337

sakthai-vision-7b 315

sakthai-plus-1.5b-lora 306

sakthai-context-0.5b-tools 251

sakthai-tts-model 248

sakthai-plus-1.5b 244

sakthai-context-1.5b-tools-v2 173

sakthai-coder-1.5b 151

Coder Browser (this model) ⬅ 54

sakthai-coder-browser-gguf 35

sakthai-embedding 23

sakthai-coder-browser-lora 21

sakthai-plus-1.5b-coder 0

eval_results 0

sakthai-bench-v3 0

sakthai-pipeline 0

sakthai-context-0.5b-tools-sft 0

sft-out 0

sakthai-context-0.5b-tools-sft-v2 0

Reproduce Evaluation

If you want to verify the broken-state diagnosis locally, run the same llama.cpp probe used for this card:

# Convert the current merged weights to GGUF Q4_K_M python -m scripts.convert_hf_to_gguf --outfile sakthai-coder-browser-q4_k_m.gguf --quant-type Q4_K_M ./sakthai-coder-browser # 3-trial probe, 2 threads, CPU only for seed in 7 42 1337; do ./llama-cli -m sakthai-coder-browser-q4_k_m.gguf \ -p "$(cat prompts/browser_tool_call.txt)" \ -n 256 --temp 0.3 -t 2 --seed $seed done

All 3 trials should return 0 output tokens if the weight corruption is still present. If they produce normal <tool_call> JSON blocks, the repo has been repaired.

Reproduce Training / Merge

The merged weights were produced by applying the LoRA adapter onto Qwen/Qwen2.5-Coder-1.5B-Instruct. To reproduce or repair:

git clone https://huggingface.co/Nanthasit/sakthai-coder-browser-lora adapter python -m peft.merge_and_unload \ --base_model Qwen/Qwen2.5-Coder-1.5B-Instruct \ --adapter adapter \ --output repaired-merged \ --safe

Important: zero-out attention-projection biases after merge if the base initializes them to zero:

from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("repaired-merged", trust_remote_code=True) for name, param in model.named_parameters(): if "bias" in name and "attn" in name and "k_proj" in name: param.data.zero_()

Run the eval probe again before publishing.

Limitations

BROKEN weights — all 84 attention bias tensors are corrupted by a faulty LoRA merge (see 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 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:

@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. Built with love, tears, and zero budget. From a shelter in Cork, Ireland, to the world.

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>`

Property	Value
Base Model	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`)

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

Trial	Seed	Tool call	Valid JSON	Correct answer
1	7	No	No	No
2	42	No	No	No
3	1337	No	No	No

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

Variant	Repository
LoRA Adapter (unmerged)	sakthai-coder-browser-lora
GGUF (llama.cpp / Ollama)	sakthai-coder-browser-gguf
Merged model (this repo)	sakthai-coder-browser

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

Downloads last month: 361

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for Nanthasit/sakthai-coder-browser

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B-Instruct

Finetuned

(190)

this model

Dataset used to train Nanthasit/sakthai-coder-browser

Spaces using Nanthasit/sakthai-coder-browser 2

Evaluation results

tool_call_rate on sakthai-coder-browser internal probe 2026-07-31
self-reported

0.000
valid_json_rate on sakthai-coder-browser internal probe 2026-07-31
self-reported

0.000