pjev-2b: MiniCPM5-2B + LoRA r64, letter-logit readout, merged standalone

README.md

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+---
+license: apache-2.0
+base_model: openbmb/MiniCPM5-2B
+datasets:
+  - caiovicentino1/eikos-decisions
+tags:
+  - typed-decisions
+  - decision-model
+  - system-one
+  - jev
+  - jevbench
+language:
+  - en
+library_name: transformers
+---
+
+# pjev-2b
+
+A small typed-decision model. Give it evidence, a question and a fixed set of allowed answers; it
+returns a calibrated probability for every option in one forward pass. It never generates text, so
+there is nothing to parse and nothing to hallucinate.
+
+The readout is the base model's own output layer. Options are rendered `A.`, `B.`, `C.`, the prompt
+ends where the answer letter goes, and the logits at that single position are restricted to the
+letters in play and softmaxed. **No decision head is added and no new parameters are introduced** —
+which is why the pretrained model's sense of its own uncertainty survives fine-tuning instead of
+being relearned from scratch.
+
+This repository holds the merged standalone model: base weights with the adapter folded in,
+tokenizer, and calibration file. No separate adapter download, no base-model download.
+
+[Base model](https://huggingface.co/openbmb/MiniCPM5-2B) ·
+[Training data](https://huggingface.co/datasets/caiovicentino1/eikos-decisions) ·
+[JevBench](https://github.com/fstandhartinger/jevbench)
+
+## Where it stands
+
+[JevBench](https://github.com/fstandhartinger/jevbench)'s 231 public items, scored with the
+benchmark's own modules, one H100, in-process, batch 1. Competitor figures are recomputed from the
+benchmark's published per-item records on the identical items, except imajev-4b, which publishes
+aggregates only.
+
+| System | Params | Easy | Standard | **Hard** |
+|---|---:|---:|---:|---:|
+| Jev 1.13.0 (commercial) | — | 100.0 | 98.6 | 73.0 |
+| imajev-4b | 4B | 100.0 | 98.6 | 72.1 |
+| djev | — | 100.0 | 98.6 | 67.6 |
+| **pjev-2b** | **2.5B** | **100.0** | **81.9** | **61.3** |
+| SemIf | 4B | 100.0 | 98.6 | 61.3 |
+| jqv | 32B | 100.0 | 95.8 | 61.3 |
+| reflex-4B | 4B | 100.0 | 94.4 | 60.4 |
+| kev 8B | 8B | 100.0 | 93.1 | 45.0 |
+| kev 0.6B | 0.6B | 100.0 | 80.6 | 43.2 |
+| kev 4B | 4B | 100.0 | 88.9 | 36.9 |
+| Laya | 0.4B | 95.8 | 69.4 | 35.1 |
+
+**pjev-2b is not the most accurate model in this table, and three systems above it are.** What it
+is: the smallest model here that holds the 4B line. On the hard tier it beats every published kev
+checkpoint including the 8B — four times its size — and it ties the entire 4B class at roughly half
+their parameter count.
+
+Paired on the 111 hard items, McNemar's exact test with a 20,000-sample paired bootstrap:
+
+| Opponent | Diff | 95% CI | p |
+|---|---:|---|---:|
+| kev 0.5B | +31.5 | +19.8, +43.2 | <0.0001 |
+| Laya | +26.1 | +12.6, +38.7 | 0.0003 |
+| kev 4B | +24.3 | +14.4, +34.2 | <0.0001 |
+| kev 0.6B | +18.0 | +6.3, +29.7 | 0.0055 |
+| kev 8B | +16.2 | +5.4, +27.0 | 0.0051 |
+| SemIf / jqv / reflex-4B | ±0.9 | — | ≥0.28 |
+
+These are public-half numbers and are **not** an official JevBench score. The official board also
+scores 308 sealed items that only its operator can run, and every ranked system drops substantially
+from public to sealed accuracy. Treat this as a setup check, not a ranking.
+
+## Quick start
+
+```bash
+pip install "vllm>=0.30.0"
+hf download Kailune-AI/pjev-2b --local-dir pjev-2b
+vllm serve ./pjev-2b --port 8000 --served-model-name pjev-2b \
+  --dtype bfloat16 --max-model-len 16384 --enable-prefix-caching \
+  --logprobs-mode processed_logprobs --max-logprobs 32
+```
+
+It also loads directly in `transformers` with `AutoModelForCausalLM.from_pretrained("Kailune-AI/pjev-2b")`
+— the architecture is stock `LlamaForCausalLM`, so nothing custom is required to run it.
+
+A request and its real response:
+
+```python
+import requests, json
+
+state = "Ticket: 'Charged twice for one order, need the duplicate refunded.'"
+questions = {
+    "queue": {"type": "choice", "instructions": "Route this ticket.",
+              "criteria": {"billing": None, "shipping": None, "account": None, "other": None}},
+    "urgent": {"type": "noul", "instructions": "This needs attention within 24 hours."},
+}
+r = requests.post("http://127.0.0.1:8000/v1/systemone",
+                  json={"state": state, "questions": questions})
+print(json.dumps(r.json()["answers"], indent=2))
+```
+
+```json
+{
+  "queue": {
+    "type": "choice",
+    "choice": "billing",
+    "probabilities": {"billing": 0.981, "shipping": 0.004, "account": 0.011, "other": 0.004},
+    "confidence": 0.981
+  },
+  "urgent": {"type": "noul", "noul": 0.874}
+}
+```
+
+Read it as: route to billing, and the "urgent" flag is a probability you threshold yourself against
+your own cost of being wrong. There is no verdict to parse and no confidence the model invented.
+
+## What to expect
+
+**Accuracy.** Strong on easy and standard items, mid-field on hard ones. If your decisions look
+like routing, classification, policy checks against a short document, or yes/no judgements with
+clear criteria, this is the right size. If they need multi-hop reasoning over long policies, the
+hard-tier number above is the honest guide.
+
+**Confidence.** Calibration is the axis where this model does not lead. Its hard-tier expected
+calibration error is 0.120; several competing systems are better there, and the ones that are fit a
+temperature on a held-out set. We ship at temperature 1.0 — see below — which is a defensible
+default and also a measurable cost on the hardest items.
+
+**Speed.** 20.5 ms per decision on one H100, one question at a time. Several questions about the
+same evidence share a prefix: six questions over one state take 48 ms in total, 8.0 ms each.
+
+**Options.** Up to 26 options are read directly as letters. Beyond that a tournament round is used,
+which is an approximation rather than a single joint softmax.
+
+**Abstention.** There is none. If the evidence supports no option, the model still distributes
+probability across the options you supplied. Threshold on confidence and route the low end to a
+person.
+
+## How it works
+
+1. The state, the question and the option list are rendered into a prompt that ends immediately
+   before the answer letter.
+2. One forward pass. At that final position, the logits of the tokens `A`, `B`, `C`, … are gathered
+   — each is a single token in this tokenizer, asserted at load — and everything else is discarded.
+3. Softmax over the letters in play gives the distribution. For `noul` questions the two letters are
+   the yes and no branches; for `score` questions they are the ordered levels.
+4. Nothing is decoded. One request with several questions about the same state runs the shared
+   prefix once and batches the per-question suffixes.
+
+**Why temperature 1.0.** Our held-out calibration folds turned out easier than genuinely hard
+items, so every temperature fitted on them came out below 1 and made the model more confident
+exactly where it should have been less. Rather than ship a temperature that flatters the easy
+cases, we ship uncalibrated and say so. If you have a few hundred labelled examples of your own
+traffic, fitting one temperature on them is the first improvement to try, and on our own hard-tier
+numbers it is worth real points.
+
+## Training
+
+LoRA rank 64, alpha 128 on every linear layer with the base frozen; one epoch, effective batch 32,
+learning rate 1e-4. The objective is soft cross-entropy against the corpus's full teacher
+probability distributions — never one-hot — plus a rationale auxiliary loss at weight 0.3 that is
+discarded at inference, plus per-example option-order reshuffling so position cannot carry signal.
+Held out of training entirely: one whole task family (trade-offs), one whole topic (healthcare
+administration), and one whole language (Spanish).
+
+The merged weights here were checked against the unmerged base-plus-adapter on 40 JevBench easy and
+standard items: identical answers on 40 of 40, maximum probability difference 0.0000.
+
+### Training data is not ours
+
+We did not build a corpus. This model is fine-tuned on the public
+[`caiovicentino1/eikos-decisions`](https://huggingface.co/datasets/caiovicentino1/eikos-decisions)
+dataset (CC BY 4.0), used as released with its per-row attribution intact: 23,562 core rows plus
+1,568 long-context rows. That corpus, and the recipe it documents, are the work of its author and
+should be credited there rather than here. Our contribution is the adapter, the evaluation and the
+audit below.
+
+## Contamination
+
+**JevBench: 0 of 25,130 training rows flagged.** Using the check the dataset's author published —
+word 8-grams over state plus question instructions, containment in both directions, flagged above
+0.15 with at least 8 shared grams, suite boilerplate dropped — nothing is flagged against the 231
+public items. Maximum containment anywhere is 0.0398, about a quarter of the threshold. The result
+holds at every threshold from 0.05 to 0.30, with the minimum shared-gram rule at 1 or 8, and with
+the boilerplate filter on or off.
+
+Positive controls fire, which is why we trust the zero: a real public item injected verbatim into
+the training rows flags at containment 1.000, and the same item buried in the middle of an
+18,000-word unrelated dossier also flags at 1.000, through the reverse direction alone, with a
+forward containment of 0.0012.
+
+The 231 public items played no part in developing this model. It was trained on 2026-09-24 and its
+artifacts have been frozen since; the benchmark was first cloned onto our machine three and a half
+days later. Its dev set is a deterministic hash split of the training corpus — 1,280 ids, all of
+which resolve to corpus rows and none to a benchmark item. Only one model was ever trained, so no
+checkpoint was selected on any benchmark score.
+
+**FinEntity: disclosed, and checked.** Our snapshot predates 2026-09-27, when the dataset's author
+removed 1,994 FinEntity-derived rows (968 distinct documents) because they are the test items of the
+Decision Index benchmark, and asked that they be excluded from future training including on earlier
+revisions. Those rows are in this model's training data. We therefore report no FinEntity or
+Decision Index result at all, and we checked whether they inflate anything we do report: all 968
+distinct documents were normalised and exact-matched against every suite we run — JevBench public
+(231), DecisionBench (586), a general battery (2,696), finance (890) and finance-2 (700) — with
+zero overlap in all five. Those rows also carry ODC-BY-1.0 rather than CC BY 4.0, so the corpus is
+not uniformly one licence.
+
+## Limitations
+
+- **Mid-field on hard reasoning.** Three systems in the table above beat it on the hard tier. It is
+  a 2B; this is the trade.
+- **Not the best calibrated.** Hard-tier ECE 0.120, shipped uncalibrated. Fit your own temperature.
+- **No abstention output.** Nothing to detect "the evidence does not answer this".
+- **26 options before a tournament round**, which is an approximation.
+- **English in practice.** The corpus contains Portuguese views; we evaluated in English only.
+- **Public-benchmark numbers only.** We have no measurement on any sealed evaluation, and every
+  ranked system on the public board drops substantially when one is run.
+- Not a safety, medical, legal or hiring certificate. It returns probabilities over options you
+  supply.
+
+## Licence
+
+Apache-2.0, matching the [`openbmb/MiniCPM5-2B`](https://huggingface.co/openbmb/MiniCPM5-2B) base.
+The training corpus is CC BY 4.0 with an ODC-BY-1.0 subset as noted above; please carry that
+attribution if you build on this.

calib.json

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+{"b": 0.0, "w": [0, 0, 0, 0], "features": ["log_ntok", "log_nopts", "noul", "score"], "t_global": 1.0, "mode": "T1"}

chat_template.jinja

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+{{- bos_token }}{%- if tools %}
+    {%- set tool_definitions %}
+        {{- "# Tools\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+        {%- for tool in tools %}
+            {{- "\n" }}
+            {{- tool | tojson(ensure_ascii=False) }}
+        {%- endfor %}
+        {{- '\n</tools>\n\nTool usage guidelines:\n- You may call zero or more functions. If no function calls are needed, just answer normally and do not include any <function ... </function>.\n- When calling a function, return an XML object within <function ... </function> using:\n<function name="function-name"><param name="param-name">param-value</param></function>\n- param-value may be multi-line. If it contains <, & or newline characters, wrap it in a CDATA block: <param name="param-name"><![CDATA[...multi-line value...]]></param>' }}
+    {%- endset %}
+    
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {%- if '<tool_def_sep>' in messages[0].content %}
+            {{- messages[0].content.replace('<tool_def_sep>', tool_definitions) }}
+        {%- else %}
+            {{- messages[0].content + '\n\n' + tool_definitions }}
+        {%- endif %}
+    {%- else %}
+        {{- tool_definitions.lstrip() }}
+    {%- endif %}
+    {{- '<|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 %}
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+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
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+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
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+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
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+            {%- set min_count = [tool_calls_count, tool_sep_count]|min %}
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+                    {%- set single_tool_xml %}
+                        {{- '<function name="' ~ tool_call.name ~ '">' }}
+                        {%- if tool_call.arguments %}
+                            {%- set args_dict = tool_call.arguments %}
+                            {%- for param_name, param_value in args_dict.items() %}
+                                {{- '<param name="' ~ param_name ~ '">' }}
+                                {%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
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+                                {%- else %}
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+                                {%- endif %}
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+                    {%- set processed_content = processed_content + single_tool_xml + content_parts[i] %}
+                {%- else %}
+                    {%- set processed_content = processed_content + content_parts[i] %}
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+            {%- endfor %}
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+            {%- if tool_calls_count > tool_sep_count %}
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+                            {%- for param_name, param_value in args_dict.items() %}
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+                                {%- else %}
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+                                {{- '</param>' }}
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+                        {%- endif %}
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+                    {%- endset %}
+                    {%- set processed_content = processed_content + remaining_tool_xml %}
+                {%- endfor %}
+            {%- endif %}
+            
+            {%- set content = processed_content %}
+        {%- endif %}
+        
+        {%- if reasoning_content %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+        {%- elif '<think>' not in content and '</think>' not in content %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n\n</think>\n\n' + content.lstrip('\n') }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
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+        {%- if message.tool_calls and not has_tool_sep %}
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+                {%- if tool_call.arguments %}
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+                    {%- for param_name, param_value in args_dict.items() %}
+                        {{- '<param name="' ~ param_name ~ '">' }}
+                        {%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
+                            {{- '<![CDATA[' + param_value + ']]>' }}
+                        {%- else %}
+                            {{- param_value }}
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+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {%- if message.content is string %}
+            {{- content }}
+        {%- else %}
+            {{- message.content | tojson(ensure_ascii=False) }}
+        {%- endif %}
+        {{- '\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 %}
+        {%- if enable_thinking is false %}
+            {{- '<think>\n\n</think>\n\n' }}
+        {%- elif enable_thinking is true %}
+            {{- '<think>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,35 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dtype": "bfloat16",
+  "eos_token_id": [
+    1,
+    130073
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 42,
+  "num_key_value_heads": 2,
+  "pad_token_id": 1,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 5000000,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.17.0",
+  "use_cache": true,
+  "vocab_size": 130560
+}

decision_config.json

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+{
+ "prompt_version": "letter-v1-semif",
+ "readout": "letter-logit",
+ "calib": "calib.json"
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "do_sample": true,
+  "eos_token_id": [
+    1,
+    130073
+  ],
+  "pad_token_id": 1,
+  "temperature": 1.0,
+  "top_p": 0.95,
+  "transformers_version": "5.17.0"
+}

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