GoMini / README.md

GoMini v6: continues v5 on AgentGo 0.1.6 practice pages and agent-labelled steps

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---
license: apache-2.0
language:
  - en
base_model: doofz/THX-01
base_model_relation: finetune
datasets:
  - osunlp/Mind2Web
library_name: custom
pipeline_tag: text-classification
inference: false
tags:
  - modernbert
  - browser-agent
  - decision-making
  - agentgo
  - full-fine-tuning
  - safetensors
  - gguf
  - development-checkpoint
---

![GoMini v6: picks the next browser step, on your computer](assets/banner.svg)

# GoMini v6

**A small, local decision model for a bounded browser loop.**

GoMini is a full fine-tune of [THX-01](https://huggingface.co/doofz/THX-01)
for selecting the next action from an explicit set of valid candidates. It is
designed for **AgentGo browser** workflows: a larger model supplies a bounded
subgoal; GoMini chooses among prepared actions; AgentGo executes permitted
actions and checks their effects. Ordinary verified steps can stay local, with
the larger model called when the subgoal finishes or needs help.

Created by [Mhmadhassnz](https://huggingface.co/Mhmadhassnz).

> **Development release, not a passed production gate.** V6 continues v5 on
> 29,000 decisions: v5's set plus 12,000 new ones recorded in AgentGo 0.1.6 on
> new practice families (rows of identical buttons, scroll until found, retry
> after an error, covering dialogs, wait until enabled, "the 3rd option",
> focus a box, collapsed sections) and 577 steps of a large model on MiniWoB++
> pages it solved. On held-out practice pages it picks the right step
> **96.6%** of the time (v5 on the same pages: 76.6%). Through AgentGo 0.1.6's
> delegate loop, on BrowserGym MiniWoB++ pages it never saw, it finishes
> **53 of 65** subgoals (v5 on 0.1.6: 42; v5 on 0.1.5: 39). Calibrated
> fallback reliability and an independent final audit remain pending.

| Model | GoMini v6 (continues v5) |
| --- | --- |
| Foundation | `doofz/THX-01` · ModernBERT decision architecture |
| Parameters | 321.9M total; 321.7M optimized |
| Fine-tuning | Full encoder, embeddings and decision head; no LoRA |
| Validated task | One bounded `choice` question named `next` |
| Input contract | 1,024 packed tokens; 2–24 explicit choices |
| Formats | Complete native Safetensors checkpoint + Q8_0 GGUF |
| Training hardware | AMD Radeon RX 7800 XT · 16 GB |
| License | Apache 2.0; dataset attribution retained separately |

## What GoMini does

GoMini scores supplied candidates in a non-autoregressive forward pass. Choices
can represent a click, a supplied field/value pair, a prepared read method, or
explicit `wait`, `done`, `escalate` and `stop` controls. It does not generate an
arbitrary plan, new selector, tool argument, value or chat response.

```text
Large model: bounded goal + completion condition + permissions
                              |
AgentGo: observe -> prepare valid choices -> GoMini selects
             ^                                 |
             |                    independent permission checks
             |                                 |
             +----- verify actual effects <- execute allowed choice
                              |
                   verified done / needs_help / stopped
                              |
                         return to planner
```

The release contains reference selection clients, not a complete production
AgentGo integration or permission system. Every client response carries
`execution_authorized: false`; the supervisor remains responsible for execution.

## Results at a glance

### v6

![GoMini v5 to v6: right step on held-out practice pages 76.6% to 96.6%, MiniWoB++ subgoals 42 to 53 of 65, false done 3 to 4, the eight new step kinds 59.2% to 95.8%](assets/results.svg)

| Evaluation | v5 | v6 | Scope |
| --- | ---: | ---: | --- |
| Held-out practice pages (769 decisions, v5 and v6 families) | 76.6% | **96.6%** | Whole pages never trained on |
| ↳ retry after an error | 33.3% | **100%** | an optimistic toggle the server rejects once |
| ↳ rows of identical buttons | 58.8% | **100%** | "Dismiss" for the named notification |
| ↳ wait until enabled | 58.6% | **100%** | a button enabled after a moment |
| ↳ focus a box | 59.1% | **100%** | put the cursor in a named field |
| ↳ covering dialog | 66.7% | **100%** | close "What's new" first |
| ↳ collapsed sections | 78.6% | **100%** | open the right section, then click |
| ↳ scroll until found | 60.0% | 91.7% | a link further down a long page |
| ↳ "the Nth option" | 58.3% | 75.0% | unlabelled radio buttons |
| ↳ tabs | 80.6% | 75.0% | |
| ↳ tree | 73.5% | 85.7% | |
| MiniWoB++ subgoals through AgentGo 0.1.6 (13 task types x 5 seeds) | 42 / 65 | **53 / 65** | Held-out benchmark pages, Q8_0 on Vulkan, one delegation per task |
| False "done" in those 65 | 3 | 4 | GoMini said done but the page did not count the task |

Per task (v5 -> v6): focus-text 0 -> 4/5, click-collapsible 0 -> 4/5,
social-media 0 -> 3/5; click-tab-2 2/5 and form-sequence-2 0/5 for both (the
latter's instruction never says to submit).

### Inside AgentGo, driven by a large model

One model (gpt-6-luna) drives AgentGo on 38 tasks (24 BrowserGym MiniWoB++
tasks and 14 single-page-app tasks), a fresh session per task, both setups
running at the same time, twice (76 runs each). AgentGo 0.1.6 with GoMini v6
solves 94.7% of the runs with no wrong action, against 86.8% and 6 wrong
actions for AgentGo 0.1.5 with GoMini v5. The planner did not delegate to
GoMini in these runs, so they measure the browser with GoMini installed; what
GoMini does on its own is the sweep above. Details:
[AgentGo 0.1.6 benchmarks](https://github.com/Moha2005269/agentgo/blob/main/docs/benchmarks/agentgo-0.1.6.md).

![Agent benchmark: AgentGo 0.1.5 with GoMini v5 against 0.1.6 with GoMini v6, 38 tasks, 76 runs each](assets/bench-agents.svg)

### v5 (kept for reference)


| Evaluation | v4 | v5 | Scope |
| --- | ---: | ---: | --- |
| Held-out practice pages (794 decisions) | 48.7% | **96.0%** | Whole pages never trained on; forms, trees, check lists, tabs, icons, menus, sign-ins, payment buttons |
| MiniWoB++ subgoals through AgentGo (13 task types x 5 seeds) | 15 / 65 (AgentGo 0.1.4) | **39 / 65** (AgentGo 0.1.5) | Held-out benchmark pages, Q8_0 on Vulkan, one delegation per task |
| False "done" in those 65 | 26 | **3** | GoMini said done but the page did not count the task |
| AgentGo 24-form benchmark | 24 / 24 | **24 / 24** | One delegation per form |
| AgentGo install self-test | pass | **pass** | `example_request.json` |

Per-family and per-task numbers are in [EVALUATION.md](EVALUATION.md).

### v4 (kept for reference)

These are **native v4 development results**. Local suites used actual model
selections and AgentGo execution/readback on owned procedural pages. They are
not public-web accuracy, independent test scores or Q8 browser scores.

| Development evaluation | Result | Scope |
| --- | ---: | --- |
| Single-action episodes | 400 / 400 · 100% | Local click/fill/select/check and control scenarios |
| Multi-step form episodes | 133 / 133 · 100% | 471 decisions; local draft and completion checks |
| Reveal-only viewport episodes | 200 / 200 · 100% | 352 decisions; reveal without activation |
| Strict-navigation episodes | 300 / 300 · 100% | 650 decisions; prescribed scroll/back/wait policy |
| Prepared read-method episodes | 377 / 380 · 99.21% | 19 canonical read methods, prepared arguments |
| ↳ Element geometry subgroup | **17 / 20 · 85%** | Confusion with viewport/HTML reads |
| Mixed offline accepted decisions | 17,502 / 17,590 · 99.50% | Mostly procedural/synthetic examples |
| ↳ Adapted human snapshots, accepted | **365 / 441 · 82.77%** | Mind2Web-derived, target included in shortlist |
| ↳ Adapted human snapshots, all raw inputs | **365 / 504 · 72.42%** | 63 packing rejections counted as failures |

Zero false-done predictions were observed in the listed native action, form,
viewport and navigation suites. These include scenarios where a correct stop or
escalation is success; they do not prove universal task completion or reliable
failure detection. Navigation's oracle requires explicit scrolling even when a
browser click could auto-reveal its target.

**Q8_0 delivery smoke:** 64 attempted synthetic/TRAIN-derived inputs; **56
supported inputs matched both exact token IDs and native top choices**. Eight
inputs were rejected before Q8 decision inference by the packing guard. Maximum
absolute probability drift on compared inputs was 0.0008011311. This checks
conversion and interfaces, not browser success.

Read [EVALUATION.md](EVALUATION.md) for every native episode subgroup, all 19 read
methods, offline source breakdowns, rejection counts, methodology and remaining
evaluation. Machine-readable summaries and the original packing failure are in
[reports/](reports/).

## Download

| Artifact | Size | Use |
| --- | ---: | --- |
| [native/model.safetensors](native/model.safetensors) | 643,835,524 bytes | Full native checkpoint; download its configs/tokenizer/runtime too |
| [gguf/GoMini-v6-Q8_0.gguf](gguf/GoMini-v6-Q8_0.gguf) | 356,866,560 bytes | Decision-capable GGUF runtime only (SHA-256 `b90ce841…5a49bb6`) |
| [adapter/](adapter/) | Python helpers | Shared packing and resident selection clients; no learned adapters |
| [runtime/thx01/](runtime/thx01/) | Python implementation | Bundled upstream native inference code |

Use revision **`v6`** for the tagged release (`v5` and `v4` stay available). Checksums are in
[SHA256SUMS.txt](SHA256SUMS.txt), with conversion identities and status in
[PROVENANCE.json](PROVENANCE.json). No Q4 or intermediate F16 GGUF is distributed.

## Quickstart

Use a compatible **GPU PyTorch** environment with Transformers, Safetensors and
NumPy. The recorded environment and Q8 server requirements are in
[ENVIRONMENT.md](ENVIRONMENT.md). CPU portability and other GPU stacks have not
been validated by this release. It is a custom decision checkpoint; loading it
as `AutoModelForCausalLM` or sending chat-completion messages is not supported.

Download and run the included single-request example with that environment:

```python
import subprocess
import sys
from pathlib import Path
from huggingface_hub import snapshot_download

root = Path(snapshot_download("Mhmadhassnz/GoMini", revision="v6"))
subprocess.run([sys.executable, str(root / "examples" / "choose.py")], check=True)
```

The example reads [example_request.json](example_request.json), serializes it to
one line and asks the native chooser to select a candidate. It does not execute
browser actions. For an integration, keep `adapter/choose_native.py` resident:
write one compact JSON object per line to stdin and read one JSON result per line
from stdout. Diagnostic messages use stderr.

For Q8_0, first start a compatible **ModernBERT decision-capable llama.cpp
server** with `/tokenize` and `/v1/systemone`, following
[ENVIRONMENT.md](ENVIRONMENT.md). Its binary is not included. Then run:

```python
subprocess.run([
    sys.executable, str(root / "examples" / "choose.py"),
    "--q8", "--url", "http://127.0.0.1:8765",
], check=True)
```

Use `adapter/q8_client.py` for a resident guarded Q8 client. Keep its packing and
runtime token checks: some literal-special-token/trailing-whitespace inputs do
not match native per-piece packing and must escalate. Native/Q8 agreement on the
limited smoke suite does not establish equivalence for every possible input.

## Integrating with AgentGo

The recommended loop and implementation details are in
[LIMITATIONS_AND_AGENTGO_INTEGRATION.md](LIMITATIONS_AND_AGENTGO_INTEGRATION.md).

1. Have the large model specify the subgoal, completion predicate, permitted tab
   and origin, exact supplied values, effect permissions, deadline and step budget.
2. Build a fresh compact observation and bind valid candidate keys to prepared
   browser operations. Include explicit wait, stop, done and escalation controls.
3. Use the shared `gomini.agentgo/1` packer. Keep the 512-token instruction/options
   head budget, 1,024-token total, maximum 24 choices and 48 tokens per rendered
   option. Reject oversize input rather than silently truncating it.
4. Independently check permissions, tab/origin/revision, candidate membership,
   exact field/value binding and attempt limits before executing any selection.
5. Verify effects from browser evidence. Stop on cancellation; return compact
   evidence to the larger model on verified completion, ambiguity, missing
   information, repeated failure or exhausted budget.

For a known fixed request such as an element rectangle, deterministic dispatch
to the correct prepared method can be better than asking a model to rediscover
the mapping. A successful command or HTTP 200 does not prove goal completion.

## Limitations

- **General browsing is unproven.** Local fixtures share families and some markup;
  split-separated local labels are not independent real websites or accounts.
- **Human transfer is weaker than the procedural average.** The adapted dataset
  includes the annotated target in each shortlist. Its scores are not official
  Mind2Web benchmark results or full browser trajectories.
- **1,088 registry names are not 1,088 verified tools.** Training includes synthetic
  description routing, including aliases; actual read-method checks cover 19
  canonical methods, with prepared arguments and no write-operation coverage.
- **Confidence is uncalibrated.** V4 uses temperature 1.0; confident mistakes
  occurred. Probability, margin and entropy must not grant permission. The unused
  auxiliary action head was not supervised and must not gate safety.
- **Fallback is an unmeasured property.** Do not assume every failed action will
  request help. Validate independent checks and larger-model handoffs separately.
- **Q8 browser evaluation and final audit remain pending.** Packing guards reject
  some inputs; reference clients are not a hardened supervisor. No general >90%
  success, latency, multilingual retention or production-readiness claim is made.

## Training and provenance

V6 is a full fine-tune of the v5 checkpoint on 28,317 decisions (v5's
14,902-record set plus 12,049 new teacher records on 0.1.6's practice pages
and 577 agent-labelled steps; the best of 2 epochs by held-out pages, batch
8 x 4 accumulation, AdamW 3e-6, same objective). The agent-labelled steps are
the actions of gpt-6-luna on MiniWoB++ pages outside every evaluation set,
taken only from runs the page scored as solved; held-out records are whole
pages (seeds 39001-39300). No evaluation page (the 24 agent-benchmark tasks,
the 13 GoMini sweep task types, the 14 SPA tasks) was used.

V5 is a full fine-tune of the v4 checkpoint on 14,902 new decisions (2 epochs,
AdamW 3e-6, same objective), recorded by a teacher on 6,000 procedurally
generated practice pages served to AgentGo 0.1.5 and packed with AgentGo's
own packer; held-out records are whole pages. No MiniWoB++ or other benchmark
page was used. The recipe and per-family counts are in [TRAINING.md](TRAINING.md).

V4 continues the prior native checkpoint with **53,146 accepted unique training
records**, combining procedural examples, synthetic registry routing, adapted
Mind2Web snapshots and verified decisions on owned local AgentGo fixtures. It
reached epoch 2 / step 5,660 using AdamW, BF16 autocast, FP32 optimization and a
stable log-score plus spherical-score objective. Embeddings and all encoder
blocks were trainable; this was full fine-tuning.

See [TRAINING.md](TRAINING.md) for the recipe and source counts, and
[native/training-settings.json](native/training-settings.json) for exact data
file hashes and settings. No raw website corpus or complete collection harness
is hosted here. Development influenced selection; frozen calibration/final-test
records were not consumed for this handoff.

## Credits and license

GoMini builds on **THX-01 by HAL-X AI**, with its mmBERT encoder attribution
preserved in [NOTICE](NOTICE). Upstream weights and bundled runtime retain their
[Apache 2.0 license](LICENSE). The pinned foundation revision is
`b323afcef149f9288a9d0daef4badd67b9b87c61`.

**Mind2Web** is credited to Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Samuel
Stevens, Boshi Wang, Huan Sun and Yu Su (2023). Its dataset is CC BY 4.0; the local
adaptation changes representation, packing and split assignment. See
[THIRD_PARTY_DATA.md](THIRD_PARTY_DATA.md) and the
[official Mind2Web project](https://github.com/OSU-NLP-Group/Mind2Web).

AgentGo is the browser integration target and execution environment for the
reported local fixture checks. AgentGo browser software is not distributed in
this model repository.

	---
	license: apache-2.0
	language:
	- en
	base_model: doofz/THX-01
	base_model_relation: finetune
	datasets:
	- osunlp/Mind2Web
	library_name: custom
	pipeline_tag: text-classification
	inference: false
	tags:
	- modernbert
	- browser-agent
	- decision-making
	- agentgo
	- full-fine-tuning
	- safetensors
	- gguf
	- development-checkpoint
	---

	![GoMini v6: picks the next browser step, on your computer](assets/banner.svg)

	# GoMini v6

	A small, local decision model for a bounded browser loop.

	GoMini is a full fine-tune of [THX-01](https://huggingface.co/doofz/THX-01)
	for selecting the next action from an explicit set of valid candidates. It is
	designed for AgentGo browser workflows: a larger model supplies a bounded
	subgoal; GoMini chooses among prepared actions; AgentGo executes permitted
	actions and checks their effects. Ordinary verified steps can stay local, with
	the larger model called when the subgoal finishes or needs help.

	Created by [Mhmadhassnz](https://huggingface.co/Mhmadhassnz).

	> Development release, not a passed production gate. V6 continues v5 on
	> 29,000 decisions: v5's set plus 12,000 new ones recorded in AgentGo 0.1.6 on
	> new practice families (rows of identical buttons, scroll until found, retry
	> after an error, covering dialogs, wait until enabled, "the 3rd option",
	> focus a box, collapsed sections) and 577 steps of a large model on MiniWoB++
	> pages it solved. On held-out practice pages it picks the right step
	> 96.6% of the time (v5 on the same pages: 76.6%). Through AgentGo 0.1.6's
	> delegate loop, on BrowserGym MiniWoB++ pages it never saw, it finishes
	> 53 of 65 subgoals (v5 on 0.1.6: 42; v5 on 0.1.5: 39). Calibrated
	> fallback reliability and an independent final audit remain pending.

	\| Model \| GoMini v6 (continues v5) \|
	\| --- \| --- \|
	\| Foundation \| `doofz/THX-01` · ModernBERT decision architecture \|
	\| Parameters \| 321.9M total; 321.7M optimized \|
	\| Fine-tuning \| Full encoder, embeddings and decision head; no LoRA \|
	\| Validated task \| One bounded `choice` question named `next` \|
	\| Input contract \| 1,024 packed tokens; 2–24 explicit choices \|
	\| Formats \| Complete native Safetensors checkpoint + Q8_0 GGUF \|
	\| Training hardware \| AMD Radeon RX 7800 XT · 16 GB \|
	\| License \| Apache 2.0; dataset attribution retained separately \|

	## What GoMini does

	GoMini scores supplied candidates in a non-autoregressive forward pass. Choices
	can represent a click, a supplied field/value pair, a prepared read method, or
	explicit `wait`, `done`, `escalate` and `stop` controls. It does not generate an
	arbitrary plan, new selector, tool argument, value or chat response.

	```text
	Large model: bounded goal + completion condition + permissions
	\|
	AgentGo: observe -> prepare valid choices -> GoMini selects
	^ \|
	\| independent permission checks
	\| \|
	+----- verify actual effects <- execute allowed choice
	\|
	verified done / needs_help / stopped
	\|
	return to planner
	```

	The release contains reference selection clients, not a complete production
	AgentGo integration or permission system. Every client response carries
	`execution_authorized: false`; the supervisor remains responsible for execution.

	## Results at a glance

	### v6

	![GoMini v5 to v6: right step on held-out practice pages 76.6% to 96.6%, MiniWoB++ subgoals 42 to 53 of 65, false done 3 to 4, the eight new step kinds 59.2% to 95.8%](assets/results.svg)

	\| Evaluation \| v5 \| v6 \| Scope \|
	\| --- \| ---: \| ---: \| --- \|
	\| Held-out practice pages (769 decisions, v5 and v6 families) \| 76.6% \| 96.6% \| Whole pages never trained on \|
	\| ↳ retry after an error \| 33.3% \| 100% \| an optimistic toggle the server rejects once \|
	\| ↳ rows of identical buttons \| 58.8% \| 100% \| "Dismiss" for the named notification \|
	\| ↳ wait until enabled \| 58.6% \| 100% \| a button enabled after a moment \|
	\| ↳ focus a box \| 59.1% \| 100% \| put the cursor in a named field \|
	\| ↳ covering dialog \| 66.7% \| 100% \| close "What's new" first \|
	\| ↳ collapsed sections \| 78.6% \| 100% \| open the right section, then click \|
	\| ↳ scroll until found \| 60.0% \| 91.7% \| a link further down a long page \|
	\| ↳ "the Nth option" \| 58.3% \| 75.0% \| unlabelled radio buttons \|
	\| ↳ tabs \| 80.6% \| 75.0% \| \|
	\| ↳ tree \| 73.5% \| 85.7% \| \|
	\| MiniWoB++ subgoals through AgentGo 0.1.6 (13 task types x 5 seeds) \| 42 / 65 \| 53 / 65 \| Held-out benchmark pages, Q8_0 on Vulkan, one delegation per task \|
	\| False "done" in those 65 \| 3 \| 4 \| GoMini said done but the page did not count the task \|

	Per task (v5 -> v6): focus-text 0 -> 4/5, click-collapsible 0 -> 4/5,
	social-media 0 -> 3/5; click-tab-2 2/5 and form-sequence-2 0/5 for both (the
	latter's instruction never says to submit).

	### Inside AgentGo, driven by a large model

	One model (gpt-6-luna) drives AgentGo on 38 tasks (24 BrowserGym MiniWoB++
	tasks and 14 single-page-app tasks), a fresh session per task, both setups
	running at the same time, twice (76 runs each). AgentGo 0.1.6 with GoMini v6
	solves 94.7% of the runs with no wrong action, against 86.8% and 6 wrong
	actions for AgentGo 0.1.5 with GoMini v5. The planner did not delegate to
	GoMini in these runs, so they measure the browser with GoMini installed; what
	GoMini does on its own is the sweep above. Details:
	[AgentGo 0.1.6 benchmarks](https://github.com/Moha2005269/agentgo/blob/main/docs/benchmarks/agentgo-0.1.6.md).

	![Agent benchmark: AgentGo 0.1.5 with GoMini v5 against 0.1.6 with GoMini v6, 38 tasks, 76 runs each](assets/bench-agents.svg)

	### v5 (kept for reference)


	\| Evaluation \| v4 \| v5 \| Scope \|
	\| --- \| ---: \| ---: \| --- \|
	\| Held-out practice pages (794 decisions) \| 48.7% \| 96.0% \| Whole pages never trained on; forms, trees, check lists, tabs, icons, menus, sign-ins, payment buttons \|
	\| MiniWoB++ subgoals through AgentGo (13 task types x 5 seeds) \| 15 / 65 (AgentGo 0.1.4) \| 39 / 65 (AgentGo 0.1.5) \| Held-out benchmark pages, Q8_0 on Vulkan, one delegation per task \|
	\| False "done" in those 65 \| 26 \| 3 \| GoMini said done but the page did not count the task \|
	\| AgentGo 24-form benchmark \| 24 / 24 \| 24 / 24 \| One delegation per form \|
	\| AgentGo install self-test \| pass \| pass \| `example_request.json` \|

	Per-family and per-task numbers are in [EVALUATION.md](EVALUATION.md).

	### v4 (kept for reference)

	These are native v4 development results. Local suites used actual model
	selections and AgentGo execution/readback on owned procedural pages. They are
	not public-web accuracy, independent test scores or Q8 browser scores.

	\| Development evaluation \| Result \| Scope \|
	\| --- \| ---: \| --- \|
	\| Single-action episodes \| 400 / 400 · 100% \| Local click/fill/select/check and control scenarios \|
	\| Multi-step form episodes \| 133 / 133 · 100% \| 471 decisions; local draft and completion checks \|
	\| Reveal-only viewport episodes \| 200 / 200 · 100% \| 352 decisions; reveal without activation \|
	\| Strict-navigation episodes \| 300 / 300 · 100% \| 650 decisions; prescribed scroll/back/wait policy \|
	\| Prepared read-method episodes \| 377 / 380 · 99.21% \| 19 canonical read methods, prepared arguments \|
	\| ↳ Element geometry subgroup \| 17 / 20 · 85% \| Confusion with viewport/HTML reads \|
	\| Mixed offline accepted decisions \| 17,502 / 17,590 · 99.50% \| Mostly procedural/synthetic examples \|
	\| ↳ Adapted human snapshots, accepted \| 365 / 441 · 82.77% \| Mind2Web-derived, target included in shortlist \|
	\| ↳ Adapted human snapshots, all raw inputs \| 365 / 504 · 72.42% \| 63 packing rejections counted as failures \|

	Zero false-done predictions were observed in the listed native action, form,
	viewport and navigation suites. These include scenarios where a correct stop or
	escalation is success; they do not prove universal task completion or reliable
	failure detection. Navigation's oracle requires explicit scrolling even when a
	browser click could auto-reveal its target.

	Q8_0 delivery smoke: 64 attempted synthetic/TRAIN-derived inputs; **56
	supported inputs matched both exact token IDs and native top choices**. Eight
	inputs were rejected before Q8 decision inference by the packing guard. Maximum
	absolute probability drift on compared inputs was 0.0008011311. This checks
	conversion and interfaces, not browser success.

	Read [EVALUATION.md](EVALUATION.md) for every native episode subgroup, all 19 read
	methods, offline source breakdowns, rejection counts, methodology and remaining
	evaluation. Machine-readable summaries and the original packing failure are in
	[reports/](reports/).

	## Download

	\| Artifact \| Size \| Use \|
	\| --- \| ---: \| --- \|
	\| [native/model.safetensors](native/model.safetensors) \| 643,835,524 bytes \| Full native checkpoint; download its configs/tokenizer/runtime too \|
	\| [gguf/GoMini-v6-Q8_0.gguf](gguf/GoMini-v6-Q8_0.gguf) \| 356,866,560 bytes \| Decision-capable GGUF runtime only (SHA-256 `b90ce841…5a49bb6`) \|
	\| [adapter/](adapter/) \| Python helpers \| Shared packing and resident selection clients; no learned adapters \|
	\| [runtime/thx01/](runtime/thx01/) \| Python implementation \| Bundled upstream native inference code \|

	Use revision `v6` for the tagged release (`v5` and `v4` stay available). Checksums are in
	[SHA256SUMS.txt](SHA256SUMS.txt), with conversion identities and status in
	[PROVENANCE.json](PROVENANCE.json). No Q4 or intermediate F16 GGUF is distributed.

	## Quickstart

	Use a compatible GPU PyTorch environment with Transformers, Safetensors and
	NumPy. The recorded environment and Q8 server requirements are in
	[ENVIRONMENT.md](ENVIRONMENT.md). CPU portability and other GPU stacks have not
	been validated by this release. It is a custom decision checkpoint; loading it
	as `AutoModelForCausalLM` or sending chat-completion messages is not supported.

	Download and run the included single-request example with that environment:

	```python
	import subprocess
	import sys
	from pathlib import Path
	from huggingface_hub import snapshot_download

	root = Path(snapshot_download("Mhmadhassnz/GoMini", revision="v6"))
	subprocess.run([sys.executable, str(root / "examples" / "choose.py")], check=True)
	```

	The example reads [example_request.json](example_request.json), serializes it to
	one line and asks the native chooser to select a candidate. It does not execute
	browser actions. For an integration, keep `adapter/choose_native.py` resident:
	write one compact JSON object per line to stdin and read one JSON result per line
	from stdout. Diagnostic messages use stderr.

	For Q8_0, first start a compatible **ModernBERT decision-capable llama.cpp
	server** with `/tokenize` and `/v1/systemone`, following
	[ENVIRONMENT.md](ENVIRONMENT.md). Its binary is not included. Then run:

	```python
	subprocess.run([
	sys.executable, str(root / "examples" / "choose.py"),
	"--q8", "--url", "http://127.0.0.1:8765",
	], check=True)
	```

	Use `adapter/q8_client.py` for a resident guarded Q8 client. Keep its packing and
	runtime token checks: some literal-special-token/trailing-whitespace inputs do
	not match native per-piece packing and must escalate. Native/Q8 agreement on the
	limited smoke suite does not establish equivalence for every possible input.

	## Integrating with AgentGo

	The recommended loop and implementation details are in
	[LIMITATIONS_AND_AGENTGO_INTEGRATION.md](LIMITATIONS_AND_AGENTGO_INTEGRATION.md).

	1. Have the large model specify the subgoal, completion predicate, permitted tab
	and origin, exact supplied values, effect permissions, deadline and step budget.
	2. Build a fresh compact observation and bind valid candidate keys to prepared
	browser operations. Include explicit wait, stop, done and escalation controls.
	3. Use the shared `gomini.agentgo/1` packer. Keep the 512-token instruction/options
	head budget, 1,024-token total, maximum 24 choices and 48 tokens per rendered
	option. Reject oversize input rather than silently truncating it.
	4. Independently check permissions, tab/origin/revision, candidate membership,
	exact field/value binding and attempt limits before executing any selection.
	5. Verify effects from browser evidence. Stop on cancellation; return compact
	evidence to the larger model on verified completion, ambiguity, missing
	information, repeated failure or exhausted budget.

	For a known fixed request such as an element rectangle, deterministic dispatch
	to the correct prepared method can be better than asking a model to rediscover
	the mapping. A successful command or HTTP 200 does not prove goal completion.

	## Limitations

	- General browsing is unproven. Local fixtures share families and some markup;
	split-separated local labels are not independent real websites or accounts.
	- Human transfer is weaker than the procedural average. The adapted dataset
	includes the annotated target in each shortlist. Its scores are not official
	Mind2Web benchmark results or full browser trajectories.
	- 1,088 registry names are not 1,088 verified tools. Training includes synthetic
	description routing, including aliases; actual read-method checks cover 19
	canonical methods, with prepared arguments and no write-operation coverage.
	- Confidence is uncalibrated. V4 uses temperature 1.0; confident mistakes
	occurred. Probability, margin and entropy must not grant permission. The unused
	auxiliary action head was not supervised and must not gate safety.
	- Fallback is an unmeasured property. Do not assume every failed action will
	request help. Validate independent checks and larger-model handoffs separately.
	- Q8 browser evaluation and final audit remain pending. Packing guards reject
	some inputs; reference clients are not a hardened supervisor. No general >90%
	success, latency, multilingual retention or production-readiness claim is made.

	## Training and provenance

	V6 is a full fine-tune of the v5 checkpoint on 28,317 decisions (v5's
	14,902-record set plus 12,049 new teacher records on 0.1.6's practice pages
	and 577 agent-labelled steps; the best of 2 epochs by held-out pages, batch
	8 x 4 accumulation, AdamW 3e-6, same objective). The agent-labelled steps are
	the actions of gpt-6-luna on MiniWoB++ pages outside every evaluation set,
	taken only from runs the page scored as solved; held-out records are whole
	pages (seeds 39001-39300). No evaluation page (the 24 agent-benchmark tasks,
	the 13 GoMini sweep task types, the 14 SPA tasks) was used.

	V5 is a full fine-tune of the v4 checkpoint on 14,902 new decisions (2 epochs,
	AdamW 3e-6, same objective), recorded by a teacher on 6,000 procedurally
	generated practice pages served to AgentGo 0.1.5 and packed with AgentGo's
	own packer; held-out records are whole pages. No MiniWoB++ or other benchmark
	page was used. The recipe and per-family counts are in [TRAINING.md](TRAINING.md).

	V4 continues the prior native checkpoint with **53,146 accepted unique training
	records**, combining procedural examples, synthetic registry routing, adapted
	Mind2Web snapshots and verified decisions on owned local AgentGo fixtures. It
	reached epoch 2 / step 5,660 using AdamW, BF16 autocast, FP32 optimization and a
	stable log-score plus spherical-score objective. Embeddings and all encoder
	blocks were trainable; this was full fine-tuning.

	See [TRAINING.md](TRAINING.md) for the recipe and source counts, and
	[native/training-settings.json](native/training-settings.json) for exact data
	file hashes and settings. No raw website corpus or complete collection harness
	is hosted here. Development influenced selection; frozen calibration/final-test
	records were not consumed for this handoff.

	## Credits and license

	GoMini builds on THX-01 by HAL-X AI, with its mmBERT encoder attribution
	preserved in [NOTICE](NOTICE). Upstream weights and bundled runtime retain their
	[Apache 2.0 license](LICENSE). The pinned foundation revision is
	`b323afcef149f9288a9d0daef4badd67b9b87c61`.

	Mind2Web is credited to Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Samuel
	Stevens, Boshi Wang, Huan Sun and Yu Su (2023). Its dataset is CC BY 4.0; the local
	adaptation changes representation, packing and split assignment. See
	[THIRD_PARTY_DATA.md](THIRD_PARTY_DATA.md) and the
	[official Mind2Web project](https://github.com/OSU-NLP-Group/Mind2Web).

	AgentGo is the browser integration target and execution environment for the
	reported local fixture checks. AgentGo browser software is not distributed in
	this model repository.