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Model: markopoloaiinc/Athena-mvrko-4B
Source: Original Platform

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: other                     # ⟨FILL: Decision 0.1 — Apache-2.0 (inherited from Qwen3-4B) if open, else 'other' + license_name/link⟩
+base_model: Qwen/Qwen3-4B
+base_model_relation: finetune
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+  - opera
+tags:
+  - athena
+  - mvrko
+  - e-commerce
+  - shopping-behavior
+  - next-action-prediction
+  - large-event-model
+  - web-agents
+  - lora
+  - opera
+metrics:
+  - exact_match
+model-index:
+  - name: Athena
+    results:
+      - task:
+          type: next-action-prediction
+          name: Next-action prediction
+        dataset:
+          name: OPeRA (full official test set)
+          type: opera
+          config: exact-match
+        metrics:
+          - type: exact_match
+            value: 0.2450
+            name: Exact-match (n=992)
+            verified: false
+---
+
+# Athena (4B): a Large Event Model for shopping behavior
+
+**Athena predicts the exact next action a real shopper takes on a live retail page.**
+Given the reduced page state and the interaction history, it emits the next action as
+structured JSON. On the **full official OPeRA test set (992 actions)** it scores
+**24.50% strict exact-match, first among every model tested, ahead of GPT-5.6, Claude
+Sonnet 5, and Claude Opus 4.8**, as a 4-billion-parameter model.
+
+Athena is the **base model of the mvrko simulation track**: the accurate, cheap,
+self-hostable foundation for agentic shopping, next-action planning, and behavioral
+simulation.
+
+- **Developer:** Markopolo AI
+- **Model type:** Decoder-only causal LM (dense), LoRA fine-tune of an open base model
+- **Base model:** [`Qwen/Qwen3-4B`](https://huggingface.co/Qwen/Qwen3-4B)
+- **Modality:** reduced page state + interaction history → next action
+  (`action_type`, `semantic_id`, `input_text`)
+- **Benchmark:** OPeRA next-action prediction, strict exact-match on the target element
+- **Repository:** ⟨FILL: final repo path — Decision 1.5b⟩
+- **Release:** ⟨FILL: version string⟩
+
+> Athena is a **fine-tune of an open base model.** The value is the training recipe, the
+> observation-space engineering, the 32K long-context supervision, and a specialization a
+> general-purpose model cannot reach by prompting. The base weights are the substrate; the
+> moat is everything built on top.
+
+---
+
+## TL;DR
+
+| | |
+|---|---|
+| **Headline** | **24.50% exact-match on OPeRA (full 992)**, #1 vs current frontier |
+| **Base** | Qwen3-4B · LoRA r=32, α=32 · 32K context |
+| **Output** | Structured next-action JSON (schema below); **98.9%** schema-valid on the 992 |
+| **Reproducibility** | Reproduced cold on `transformers 5.5.0` / `torch 2.8.0` / bf16 / greedy |
+| **What it is NOT** | It does **not** emit calibrated probabilities. See [Limitations](#limitations) |
+
+---
+
+## Results: full official OPeRA test set (n = 992)
+
+Same test set (`md5 1e02a30d…`), same harness, same strict exact-match scorer for every
+model. Frontier models are evaluated zero-shot / prompted; Athena is fine-tuned.
+
+| Model | Params | Exact-match | Δ vs Athena | Margin |
+|---|---|---|---|---|
+| **Athena (ours, fine-tuned)** | **4B** | **24.50%** | | |
+| GPT-5.6 (prompted) | frontier | 22.58% | +1.92 | **parity** (≈1σ, unpaired) |
+| *GPT-4.1 (published OPeRA baseline)* | *frontier* | *21.5%* | *+3.0* | *ahead of published SOTA* |
+| Claude Sonnet 5 (prompted) | frontier | 18.35% | +6.15 | **separated** (≈3.3σ) |
+| Claude Opus 4.8 (prompted) | frontier | 12.70% | +11.80 | **separated** |
+
+**Athena ranks first, ahead of every current-frontier model tested and the published
+baseline.**
+
+### Honest statistical read
+
+- **vs Claude Opus 4.8: separated.** +11.8 points, far beyond sampling noise.
+- **vs Claude Sonnet 5: separated.** +6.15 points, ≈3.3σ.
+- **vs published GPT-4.1 baseline: ahead.** +3.0 points.
+- **vs GPT-5.6: statistical parity, nominal edge.** +1.92 points is ≈1σ (unpaired), a
+  first-place finish with a nominal lead, **not** a statistically separated one. We report
+  it as such. A paired McNemar test on the shared 992 is the correct way to sharpen this
+  and is ⟨FILL: pending — Decision 0.2⟩.
+
+> We lead the board **and** state exactly how strong each margin is. Nothing is labeled
+> "clear" unless the test supports it.
+
+### The task ceiling: what the frontier numbers reveal
+
+The entire current frontier lands between **12% and 23%** on OPeRA. The ceiling here is the
+**difficulty of the task, not model size**: predicting the *exact* element a human clicks,
+from dozens of candidates, is genuinely hard, and raw capability barely moves it. Athena's
+24.50% is not "low". It is the **best result on a benchmark where the strongest general
+models in the world sit below it.** The lever that moves this number is **behavioral
+specialization**, not scale.
+
+---
+
+## Why a specialist wins
+
+The frontier models return clean, schema-valid JSON. They understand the task perfectly.
+They still lose, because next-action prediction requires knowing *how real shoppers ground
+their intent in this interface*, and that knowledge is **behavioral, not linguistic**. A
+prompt yields a fluent guess; it cannot supply behavior the model never learned.
+
+> The frontier models are excellent at language. Athena is excellent at shoppers.
+
+**Strongest supporting evidence, the OPeRA error analysis:** the benchmark's own error
+taxonomy localizes frontier failure to *grounding* (naming the right element), not
+*formatting*. ⟨FILL: cite the specific error-type percentages with the paper section, from
+the benchmark source-fact sheet (artifact 1.7). Do not paraphrase from memory.⟩
+
+---
+
+## Architecture and the core innovation: the observation space
+
+The central innovation is not the weights. It is **how the web page is represented to the
+model.**
+
+1. **A learned observation space.** Raw HTML is unlearnable at scale, since a single page
+   blows past any practical context window. Athena consumes a **structure-preserving
+   reduction** that keeps only the *named, actionable* elements (the ones an action can
+   target) and discards the rest, turning a sprawling DOM into a compact, typed,
+   model-legible page state. This is what makes the next-action target *predictable*
+   instead of buried.
+2. **Long-context supervision at 32K.** Real sessions carry long histories and large pages;
+   Athena trains at a **32,768-token** context so it conditions on the full journey, not a
+   truncated snippet.
+3. **Behavioral fine-tuning.** Trained directly on what shoppers do over this action space,
+   with completion-only masking on the target action.
+
+The **observation-space parser** (or a specification precise enough to rebuild it) is
+released so third parties can reproduce the input format. See Reproduction. ⟨FILL: link —
+artifact in Part 2⟩
+
+---
+
+## Quickstart
+
+> Copied from a tested script. See Reproduction. Requires `transformers==5.5.0`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch, json
+
+REPO = "⟨FILL: repo id⟩"
+tok = AutoTokenizer.from_pretrained(REPO, trust_remote_code=True)
+mdl = AutoModelForCausalLM.from_pretrained(REPO, dtype=torch.bfloat16,
+        device_map="cuda", attn_implementation="sdpa", trust_remote_code=True).eval()
+
+messages = [                       # see example_inputs/ for real OPeRA cases
+    {"role": "system",  "content": "⟨exact system string — frozen prompt spec⟩"},
+    {"role": "user",    "content": "⟨reduced page state + interaction history⟩\n\n## Next action:"},
+]
+prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+enc = tok(prompt, return_tensors="pt", truncation=True, max_length=32768).to("cuda")
+out = mdl.generate(**enc, max_new_tokens=96, do_sample=False, pad_token_id=tok.pad_token_id)
+print(tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True))
+# → {"action_type": "click", "click_type": "product_link", "semantic_id": "..."}
+```
+
+---
+
+## Input / output schema
+
+**Output object** (validated on 98.9% of the 992 test outputs):
+
+```json
+{"action_type": "click", "click_type": "product_link", "semantic_id": "active_item_list.<product>.product_detail"}
+```
+
+- **`action_type`**: enum ⟨FILL: authoritative enum from schema.json (artifact 1.3)⟩
+- **`click_type`**: enum ⟨FILL: authoritative enum from schema.json⟩
+- **`semantic_id`**: the exact named target element.
+- **`input_text`**: present only for `input` actions. ⟨FILL: exact convention on non-input actions⟩
+
+**Scoring:** strict exact-match. Predicted `action_type` **and** `semantic_id` (and
+`input_text` for inputs) must equal the ground truth. No partial credit.
+
+---
+
+
+## Hardware
+
+
+**Precision:** bf16 throughout (training and evaluation).
+
+**Weights:** ~8 GB on disk (4B parameters, bf16, safetensors), loaded as a single
+merged model with no base-model dependency at inference.
+
+**Inference.** Athena runs on a single GPU. The headline evaluation was produced on one
+NVIDIA B300 at 32,768-token context with batch size 2, greedy decoding, `sdpa` attention.
+A 24 GB card is the practical floor for full 32K-context inference; shorter contexts
+(4K to 8K, sufficient for most single-page states) fit comfortably in 16 GB. Memory is
+dominated by the bf16 weights plus the KV cache, which grows linearly with context length.
+
+---
+
+## Efficiency
+
+Athena is a **self-hostable 4B** model: 1 to 2 orders of magnitude cheaper per prediction
+than prompting a frontier reasoning API, with a **direct answer and no billed reasoning
+tokens**, **batched local inference** (no per-call round-trip, no rate limits), and **data
+kept in-house**. Exact multiples are computed from the dated cost model. ⟨FILL: efficiency
+multiple + pricing date — artifact 1.8⟩
+
+---
+
+## Applications
+
+Each tagged by which family component it requires and by maturity.
+
+| Application | Needs | Maturity |
+|---|---|---|
+| Next-action prediction / autocomplete of shopper intent | **Athena** (this model) | Benchmarked |
+| Session replay: scoring a logged journey step by step | **Athena** (this model) | Benchmarked, this is how the 992-action result is measured |
+| Behavioral simulation / free-running journey rollout | **Athena** plus an interactive page-state environment | Not demonstrated. Requires an environment that returns a new page state for a novel action; OPeRA provides logged trajectories only |
+| Calibrated conversion / intent scoring | **A different family component** (calibrated intent head, AUC/ECE), **not Athena** | Separate model |
+
+> **Family note:** Athena predicts next actions and is scored on exact match. It does **not**
+> emit calibrated probabilities. The calibrated intent head (AUC/ECE) is a **separate
+> component** of the mvrko family. See the model family map. Do not attribute calibration
+> claims to this model.
+
+
+---
+
+## Training details
+
+| Setting | Value |
+|---|---|
+| Base | `Qwen/Qwen3-4B` |
+| Method | LoRA (r=32, α=32, dropout 0.1), bf16, gradient checkpointing |
+| Target modules | q,k,v,o,gate,up,down proj |
+| Context length | 32,768 tokens |
+| Objective | completion-only masking on the next-action target |
+| Epochs / LR / schedule | 1 / 1e-4 / cosine, warmup 0.03 |
+| Hardware | 2× B300, DDP via torchrun |
+| Decode (eval) | greedy (deterministic), max_new_tokens 96 |
+| ⟨FILL from config.json / training config⟩ | layers, heads, KV heads, head dim, vocab, tokens seen |
+
+---
+
+## Reproduction
+
+The evaluation harness (runner, parser, scorer, **frozen prompt file**), the
+observation-space parser, per-example outputs for all models, and 3 to 5 example inputs are
+released at markopoloaiinc/Athena-mvrko-4B. Pinned versions and the run record:
+`transformers 5.5.0`, `torch 2.8.0+cu129`, bf16, greedy, batch 2. See
+`eval_run_record.json`.
+
+**Verification status (Part 3):**
+- [x] Cold-start reproduction of the 24.50% headline (indexed, no-dedup, `md5 1e02a30d`).
+- [ ] ⟨FILL: cross-engine determinism (transformers↔vLLM agreement rate)⟩
+- [ ] ⟨FILL: batch-invariance spot check (bs 1 vs 32)⟩
+- [ ] ⟨FILL: fresh-environment quickstart test (< 15 min, by a non-author)⟩
+- [ ] ⟨FILL: adversarial read against the OPeRA paper⟩
+- [x] Schema validation: 98.9% of 992 outputs validate.
+
+---
+
+## Limitations
+
+- **Fine-tuned vs. prompted.** Athena is fine-tuned on the task; frontier baselines are
+  prompted zero-shot. This is a *specialization* comparison, the intended one, not a claim
+  about raw model capability.
+- **Observation format.** All models are scored on Athena's reduced-HTML observation space;
+  the frontier models see it cold. A different encoding could shift their numbers.
+- **GPT-5.6 margin is within noise.** +1.92 points at n=992 is a first-place tie with a
+  nominal edge, pending a paired test.
+- **This benchmark does not evidence calibration.** Exact-match measures grounding accuracy,
+  not probability quality. Athena emits no calibrated conversion signal.
+- **Strict exact-match is unforgiving** by design. Absolute scores are low because the task
+  is hard, not because any model is failing.
+
+---
+
+## Responsible use
+
+⟨FILL: intended-use scope, out-of-scope uses, data-provenance and privacy note, and that
+predictions are behavioral estimates, not guarantees.⟩
+
+---
+
+## Related work
+
+- **OPeRA**, the benchmark and its published baselines. ⟨FILL: full citation with the real
+  author list copied from arXiv — artifact 1.7⟩
+- **RL-based OPeRA methods.** ⟨FILL: name the reinforcement-learning approaches on this
+  benchmark explicitly, so the comparison table is not only "us vs. prompted frontier."⟩
+
+---
+
+## License and citation
+
+- **License:** ⟨FILL: Decision 0.1 + the actual LICENSE file. Base `Qwen/Qwen3-4B` is
+  Apache-2.0. State inherited obligations if releasing open.⟩
+- **Citation:**
+  ```bibtex
+  @misc{athena_mvrko,
+    title  = {Athena: a 4B Large Event Model for shopping-behavior next-action prediction},
+    author = {Markopolo AI},
+    year   = {2026},
+    note   = {Markopolo AI}
+  }
+  ```
+- **Contact:** ⟨FILL⟩
+
+---
+
+*Athena is a 4B Large Event Model that predicts real shopper behavior more accurately than
+the current frontier on a public benchmark: cheaply, self-hostably, and with every margin
+stated honestly. It is the foundation of the mvrko simulation track.*

chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.5.0"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:12107b4cd99f4fbad8bc8333adbb92328ac7fa8c8aef7b71f7b7dba5bccc1d49
+size 8044982080

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 11422650

tokenizer_config.json

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+{"add_prefix_space": false, "backend": "tokenizers", "bos_token": null, "clean_up_tokenization_spaces": false, "eos_token": "<|im_end|>", "errors": "replace", "extra_special_tokens": {}, "is_local": false, "model_max_length": 131072, "pad_token": "<|endoftext|>", "split_special_tokens": false, "tokenizer_class": "Qwen2Tokenizer", "unk_token": null}