Publish signed-off ConeML 810M release

Release artifacts, model card, aggregate evaluation evidence, representative samples, methodology, and frozen-evidence hash commitments.

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+coneml-810m-alpha-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+coneml-810m-alpha-f16.gguf filter=lfs diff=lfs merge=lfs -text

LICENSE.md

@@ -0,0 +1,16 @@
+# License
+
+Copyright 2026 ConeML.
+
+This release is licensed under the Creative Commons
+Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0).
+
+License terms: https://creativecommons.org/licenses/by-nc/4.0/legalcode
+
+You may share and adapt the released material subject to the attribution
+and non-commercial conditions in that license. Commercial use is not
+granted by this release. The material is provided without warranties or
+conditions beyond those required by applicable law.
+
+For commercial evaluation or licensing enquiries, contact ConeML through
+https://huggingface.co/ConeML.

README.md

@@ -0,0 +1,200 @@
+---
+license: cc-by-nc-4.0
+library_name: transformers
+pipeline_tag: text-generation
+language:
+- en
+---
+
+# ConeML 810M Alpha
+
+ConeML 810M Alpha is an 810,112,512-parameter, decoder-only language
+model trained from scratch and instruction-tuned for constrained English
+tasks. Its measured profile favors single Python functions, selected
+small-number arithmetic families, two-step word problems, and a narrow
+designated-refusal policy.
+
+Results are specific to the documented prompt formats and evaluation task
+families. They are not claims of general coding, reasoning, factual
+knowledge, or production reliability.
+
+- Architecture: LLaMA-style; 28 layers, d=1536, 12 heads (3 KV), tied
+  embeddings, 8192 context, 32,768-token BPE tokenizer
+- Pretraining corpus: 27.87B curated tokens
+- Selected pretrained-base exposure: approximately 12.32B token
+  positions, or 15.2 tokens per parameter
+- Pretraining wall time: approximately 11 days on one NVIDIA RTX 5090;
+  fine-tuning and evaluation are excluded
+- Release `model.safetensors` SHA-256:
+  `e5df8f2edd773ad079ba00d383ace81b96cbb9579d64c570db505f9efc032b76`
+- Companion release: `coneml-810m-alpha-arithmetic`, from the same base,
+  has substantially higher carry/borrow accuracy and lower function and
+  two-step accuracy
+
+## 1. Measured profile
+
+ConeML internal certification uses held-out instances from the same task
+families as the training generators. It is not independent third-party
+certification. Code is scored by executing each generated function
+against held-out tests.
+
+| held-out task family | result | n |
+|---|---:|---:|
+| Python function writing (single function, executed) | 69.0% | 300 |
+| two-step word problems | 79.0% | 500 |
+| 2-digit × 1-digit multiplication | 97.4% | 500 |
+| 1-digit multiplication | 97.8% | 45 |
+| 1-digit addition | 98.6% | 71 |
+| "Which is bigger, X or Y?" | 95.0% | 300 |
+| 2-digit subtraction with borrow | 91.0% | 500 |
+| comparison word problems | 72.6% | 500 |
+| missing addend | 71.6% | 500 |
+| 2–3-digit addition with carry | 53.2% | 500 |
+| missing factor | 17.8% | 500 |
+
+Behavioral screens:
+
+- Designated-refusal probe: 17/17 refusal prompts produced refusals, and
+  5/5 in-scope contrast prompts in matching wording were answered. This
+  small targeted probe measures adherence to that policy, not general
+  epistemic calibration.
+- Conversation-v2: 8/8 turns passed in one scripted dialogue covering
+  cross-turn arithmetic, story revision, comparison explanation, and one
+  unknown-personal-fact refusal. This is a single dialogue, not a general
+  conversation benchmark.
+- Everyday-reasoning screen: 11/20 by manual adjudication; the
+  phrase-sensitive automatic scorer recorded 6/20.
+
+## 2. Standard benchmark limits
+
+Zero-shot, greedy decoding with a 256-token generation cap. GSM8K uses
+strict final-number matching; HumanEval reports pass@1 from executed
+tests.
+
+| benchmark | this model | same base, no fine-tune |
+|---|---:|---:|
+| GSM8K (test, first 200) | 12/200 (6.0%) | 7/200 (3.5%) |
+| HumanEval (pass@1, tests executed) | 5/164 (3.0%) | 0/164 (0.0%) |
+
+These results are weak and define an important boundary: the narrower
+task-family results above do not transfer to broad GSM8K or HumanEval
+performance.
+
+## 3. Supported prompt formats
+
+Evaluated decoding is greedy with repetition penalty 1.15, as pinned in
+`generation_config.json`.
+
+```text
+# task format (single turn)
+Question: <task>
+Answer:
+
+# dialogue format (chat_template.jinja)
+User:
+<message>
+Assistant:
+```
+
+Dialogue generation ends at `<|endoftext|>`. Prompting outside these
+formats, including paraphrases, was not systematically evaluated except
+for the published raw-completion spot check.
+
+## 4. Quantized variants
+
+Q8_0 GGUF was evaluated separately under llama.cpp. That runtime and its
+stop handling differ from the bf16 harness, so the scores are not directly
+comparable and differences cannot be attributed solely to quantization.
+
+| held-out task family | Q8_0 |
+|---|---:|
+| Python function writing | 75.0% (n=300) |
+| two-step word problems | 57.0% (n=500) |
+| addition with carry | 40.2% (n=500) |
+| multiplication families | 94.8–95.6% |
+| which-bigger | 92.7% (n=300) |
+
+Use bf16 when the bf16 profile is required.
+
+## 5. Loading
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo_id = "ConeML/coneml-810m-alpha"
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForCausalLM.from_pretrained(
+    repo_id,
+    dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+prompt = "Question: What is 7 + 8?\nAnswer:"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+output = model.generate(
+    **inputs,
+    max_new_tokens=32,
+    do_sample=False,
+    repetition_penalty=1.15,
+)
+answer = tokenizer.decode(
+    output[0][inputs["input_ids"].shape[-1]:],
+    skip_special_tokens=True,
+)
+print(answer)
+```
+
+This is a text-only causal language model. Load it with
+`AutoModelForCausalLM` or `LlamaForCausalLM`, not a multimodal model class.
+
+## 6. Intended use and safety
+
+This release is intended for research, constrained local inference,
+evaluation-harness development, and experiments whose prompts match the
+documented formats. Review outputs before use.
+
+It is not intended for factual retrieval, high-stakes medical, legal, or
+financial decisions, autonomous action, or unsandboxed execution of
+generated code. Generated code may be incomplete or unsafe; inspect and
+test it in an isolated environment.
+
+## 7. Known limitations
+
+- Not a general code model: complete programs scored 0/10, executable SQL
+  1/10, and bash 1/5. The 69% result is for single functions on ConeML's
+  task-family-matched battery.
+- Addition with carry is this release's weakest internally certified
+  arithmetic family at 53.2%; the arithmetic companion scored 96.8%.
+- The model was not evaluated as a factual-QA system and refused all six
+  real-world factual prompts in the designated-refusal probe.
+- Money/decimal change-making scored 0/3 on the held-out screen; the model
+  also showed yes-bias on feasibility questions.
+- Multi-step GSM-class reasoning remained weak.
+- Frameless prompting was spot-checked, not systematically certified.
+
+## 8. Evidence and reproducibility
+
+The `eval/` directory publishes:
+
+- `summary.json`: all aggregate bf16 results used above;
+- `representative-samples.json`: deterministic, rule-selected examples
+  from ConeML-owned probes;
+- `EVALUATION_METHODOLOGY.md`: evaluation definitions and scope;
+- separate Q8_0 aggregate results; and
+- `PRIVATE_EVIDENCE_SHA256SUMS.txt`: commitments to the complete
+  row-level evidence retained by ConeML.
+
+Complete proprietary probe rows and generations are retained privately to
+avoid releasing the full measurement instruments. The hashes permit later
+disclosure to be checked against the evidence frozen at release time.
+Public GSM8K and HumanEval rows are not redistributed; only aggregate
+results are published. `SHA256SUMS.txt` covers every shipped file.
+
+## 9. License and commercial enquiries
+
+Released for non-commercial use under CC BY-NC 4.0. Commercial use is not
+granted by this release. See `LICENSE.md`.
+
+For commercial evaluation or licensing enquiries, contact ConeML through
+the [ConeML Hugging Face organization](https://huggingface.co/ConeML).

SHA256SUMS.txt

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chat_template.jinja

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+{% for message in messages %}{% if message['role'] == 'system' %}System:
+{{ message['content'] }}
+{% elif message['role'] == 'user' %}User:
+{{ message['content'] }}
+{% elif message['role'] == 'assistant' %}Assistant:
+{{ message['content'] }}{% if not loop.last %}{{ eos_token }}
+{% endif %}{% endif %}{% endfor %}{% if add_generation_prompt %}Assistant:
+{% endif %}

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config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dtype": "bfloat16",
+  "eos_token_id": 0,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 4608,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 3,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.12.1",
+  "use_cache": true,
+  "vocab_size": 32768
+}

conversion.json

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+{
+  "artifact": "ConeML/coneml-810m-alpha",
+  "conversion": "weights-only safetensors export",
+  "dtype": "bfloat16",
+  "missing_keys": [],
+  "unexpected_keys": []
+}

eval/EVALUATION_METHODOLOGY.md

@@ -0,0 +1,59 @@
+# ConeML 810M — evaluation methodology and variant lineage (public record)
+
+Two released variants share one from-scratch 810M base checkpoint and
+differ only in supervised fine-tuning; both were evaluated with the same
+bf16 instruments.
+
+- `coneml-810m-alpha` — function-writing / mixed-arithmetic profile
+- `coneml-810m-alpha-arithmetic` — carry/borrow arithmetic variant
+
+## Methodology
+
+- **ConeML internal certification**: large-n (300–500 per task family)
+  batteries of unseen instances in the trained prompt formats, greedy
+  decoding, repetition
+  penalty 1.15. Code tasks are scored by EXECUTING the generated function
+  against held-out tests; SQL by executing against a live sqlite database;
+  scripts by captured stdout. No pattern-match scoring for anything
+  execution can decide.
+- **Behavior probes**: held-out designated-refusal prompts plus in-scope
+  contrast pairs in matching wording; one separately rerun, scripted
+  eight-turn conversation; and an everyday-reasoning screen with manual
+  adjudication. Aggregate results and deterministic representative
+  ConeML-owned rows are public; complete rows are retained privately.
+- **Public benchmarks**: GSM8K (first 200, zero-shot, strict final-number
+  match) and HumanEval (pass@1, tests executed), identical harness across
+  variants and the pretrained base. Aggregate results are public;
+  benchmark rows are not redistributed.
+- **Quantized variants are evaluated separately**: each shipped GGUF carries
+  its own result table. The GGUF and bf16 harnesses differ in runtime and
+  stop handling, so score gaps are not attributed solely to quantization.
+
+## Why two variants
+
+Under fixed architecture and base checkpoint, modest differences in
+fine-tuning composition produced large, cross-surface capability
+tradeoffs (e.g., one build scores 96.8% on held-out carry addition while
+the other scores 79.0% on held-out two-step word problems and 69.0% on
+held-out function writing). Rather than presenting one checkpoint as
+dominant, each variant ships with its measured envelope and a
+recommendation for which profile
+fits which pipeline. A checkpoint's profile is measured, never inferred.
+
+## Shared limitations
+
+Function-writing is evaluated for single Python functions only — neither
+variant is a general code model (complete programs, executable SQL, and
+bash scored near zero for both and are stated out of scope). Both variants
+refused every designated refusal prompt, including real factual questions;
+this targeted behavior is not evidence of general epistemic calibration.
+Multi-step GSM-class reasoning is weak in every variant tested this cycle.
+
+## Evidence disclosure
+
+The public folders contain aggregate result tables, deterministic
+representative examples from ConeML-owned instruments, and SHA-256
+commitments to the complete row-level evidence. Full proprietary probe
+rows are retained by ConeML rather than distributed. This preserves the
+measurement instruments while allowing later disclosure to be checked
+against the evidence frozen for this release.

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eval/certification-Q8_0.json

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+{
+  "by_category": {
+    "add-1d": {
+      "correct": 65,
+      "n": 71,
+      "acc": 0.9155
+    },
+    "add-2d-carry": {
+      "correct": 201,
+      "n": 500,
+      "acc": 0.402
+    },
+    "comparison": {
+      "correct": 334,
+      "n": 500,
+      "acc": 0.668
+    },
+    "missing-addend": {
+      "correct": 355,
+      "n": 500,
+      "acc": 0.71
+    },
+    "missing-factor": {
+      "correct": 78,
+      "n": 500,
+      "acc": 0.156
+    },
+    "mul-1d": {
+      "correct": 43,
+      "n": 45,
+      "acc": 0.9556
+    },
+    "mul-2dx1d": {
+      "correct": 474,
+      "n": 500,
+      "acc": 0.948
+    },
+    "sub-borrow": {
+      "correct": 433,
+      "n": 500,
+      "acc": 0.866
+    },
+    "two-step": {
+      "correct": 285,
+      "n": 500,
+      "acc": 0.57
+    },
+    "which-bigger": {
+      "correct": 278,
+      "n": 300,
+      "acc": 0.9267
+    }
+  },
+  "runtime": "llama.cpp Q8_0 aggregate summary"
+}

eval/code-cert-Q8_0.json

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+{
+  "passed": 225,
+  "n": 300,
+  "rate": 0.75,
+  "runtime": "llama.cpp Q8_0 aggregate summary"
+}

eval/representative-samples.json

@@ -0,0 +1,403 @@
+{
+  "schema_version": 1,
+  "model": "ConeML/coneml-810m-alpha",
+  "selection_policy": {
+    "task_families": "First passing and first failing row in original order for each family, where both exist.",
+    "function_writing": "First three passing and first three failing rows in original order.",
+    "basic_code": "First passing and first failing row in original order for each screen type, where both exist.",
+    "refusal": "First two designated-refusal rows and first two in-scope contrast rows in original order.",
+    "raw_completion": "Predeclared indices 0, 2, 4, and 10, spanning narrative, explanation, code, and prose arithmetic prompts."
+  },
+  "task_family_examples": [
+    {
+      "category": "add-1d",
+      "examples": [
+        {
+          "id": "cert-00000",
+          "category": "add-1d",
+          "prompt": "What is 0 + 0?",
+          "gen": " 0.",
+          "expected": "0",
+          "ok": true
+        },
+        {
+          "id": "cert-00002",
+          "category": "add-1d",
+          "prompt": "What is 0 + 2?",
+          "gen": " 0.",
+          "expected": "2",
+          "ok": false
+        }
+      ]
+    },
+    {
+      "category": "add-2d-carry",
+      "examples": [
+        {
+          "id": "cert-00117",
+          "category": "add-2d-carry",
+          "prompt": "What is 69 + 19?",
+          "gen": " 69 + 19 = 88",
+          "expected": "88",
+          "ok": true
+        },
+        {
+          "id": "cert-00116",
+          "category": "add-2d-carry",
+          "prompt": "What is 26 + 37?",
+          "gen": " 61",
+          "expected": "63",
+          "ok": false
+        }
+      ]
+    },
+    {
+      "category": "comparison",
+      "examples": [
+        {
+          "id": "cert-02616",
+          "category": "comparison",
+          "prompt": "Bram has 14 acorns. Ines has 40 acorns. How many more acorns does Ines have than Bram?",
+          "gen": " 26.",
+          "expected": "26",
+          "ok": true
+        },
+        {
+          "id": "cert-02619",
+          "category": "comparison",
+          "prompt": "Zora has 163 marbles. Ugo has 187 marbles. How many more marbles does Ugo have than Zora?",
+          "gen": " 44.",
+          "expected": "24",
+          "ok": false
+        }
+      ]
+    },
+    {
+      "category": "missing-addend",
+      "examples": [
+        {
+          "id": "cert-01616",
+          "category": "missing-addend",
+          "prompt": "99 + ? = 192. What number goes in the blank?",
+          "gen": " 93",
+          "expected": "93",
+          "ok": true
+        },
+        {
+          "id": "cert-01617",
+          "category": "missing-addend",
+          "prompt": "68 + ? = 259. What number goes in the blank?",
+          "gen": " 92",
+          "expected": "191",
+          "ok": false
+        }
+      ]
+    },
+    {
+      "category": "missing-factor",
+      "examples": [
+        {
+          "id": "cert-02116",
+          "category": "missing-factor",
+          "prompt": "18 × ? = 702. What number goes in the blank?",
+          "gen": " 39.",
+          "expected": "39",
+          "ok": true
+        },
+        {
+          "id": "cert-02117",
+          "category": "missing-factor",
+          "prompt": "43 × ? = 1720. What number goes in the blank?",
+          "gen": " 354.",
+          "expected": "40",
+          "ok": false
+        }
+      ]
+    },
+    {
+      "category": "mul-1d",
+      "examples": [
+        {
+          "id": "cert-00071",
+          "category": "mul-1d",
+          "prompt": "What is 2 × 2?",
+          "gen": " 4",
+          "expected": "4",
+          "ok": true
+        },
+        {
+          "id": "cert-00096",
+          "category": "mul-1d",
+          "prompt": "What is 6 × 2?",
+          "gen": " 6.",
+          "expected": "12",
+          "ok": false
+        }
+      ]
+    },
+    {
+      "category": "mul-2dx1d",
+      "examples": [
+        {
+          "id": "cert-01116",
+          "category": "mul-2dx1d",
+          "prompt": "What is 82 × 3?",
+          "gen": " 82 × 3 = 246.",
+          "expected": "246",
+          "ok": true
+        },
+        {
+          "id": "cert-01172",
+          "category": "mul-2dx1d",
+          "prompt": "What is 15 × 2?",
+          "gen": " 15.",
+          "expected": "30",
+          "ok": false
+        }
+      ]
+    },
+    {
+      "category": "sub-borrow",
+      "examples": [
+        {
+          "id": "cert-00616",
+          "category": "sub-borrow",
+          "prompt": "What is 82 - 47?",
+          "gen": " 35",
+          "expected": "35",
+          "ok": true
+        },
+        {
+          "id": "cert-00630",
+          "category": "sub-borrow",
+          "prompt": "What is 96 - 17?",
+          "gen": " 69",
+          "expected": "79",
+          "ok": false
+        }
+      ]
+    },
+    {
+      "category": "two-step",
+      "examples": [
+        {
+          "id": "cert-03116",
+          "category": "two-step",
+          "prompt": "Ada had 37 ribbons, got 36 more, then lost 27. How many ribbons does Ada have now?",
+          "gen": " 37 + 36 = 73. 73 − 27 = 46. The answer is 46.",
+          "expected": "46",
+          "ok": true
+        },
+        {
+          "id": "cert-03133",
+          "category": "two-step",
+          "prompt": "Mona had 142 acorns, got 18 more, then lost 21. How many acorns does Mona have now?",
+          "gen": " 142.",
+          "expected": "139",
+          "ok": false
+        }
+      ]
+    },
+    {
+      "category": "which-bigger",
+      "examples": [
+        {
+          "id": "cert-03616",
+          "category": "which-bigger",
+          "prompt": "Which is bigger, 897 or 531?",
+          "gen": " 897 is bigger.",
+          "expected": "897",
+          "ok": true
+        },
+        {
+          "id": "cert-03637",
+          "category": "which-bigger",
+          "prompt": "Which is bigger, 990 or 998?",
+          "gen": " 990 is bigger.",
+          "expected": "998",
+          "ok": false
+        }
+      ]
+    }
+  ],
+  "function_writing_examples": [
+    {
+      "id": "code-cert-0",
+      "family": "code_strings",
+      "prompt": "Write a Python function named count_vowels that counts the vowels in the string s. Return only code.\nFor example, count_vowels('beaudedb') should return 4.",
+      "gen": " def count_vowels(s):\n    return sum(1 for ch in s.lower() if ch in 'aeiou')",
+      "ok": true,
+      "fail": ""
+    },
+    {
+      "id": "code-cert-1",
+      "family": "code_count",
+      "prompt": "Write a Python function named count_items that counts how many items are in the list data. Return only code.\nFor example, count_items([7, 6, 0, 8]) should return 4.",
+      "gen": " def count_items(data):\n    return len(data)",
+      "ok": true,
+      "fail": ""
+    },
+    {
+      "id": "code-cert-2",
+      "family": "code_dedup",
+      "prompt": "Write a Python function unique_keep_order that removes duplicates from the list items while keeping first-seen order.\nFor example, unique_keep_order([5, 2, 3, 4, 2, 3, 2, 4]) should return [5, 2, 3, 4].",
+      "gen": " def unique_keep_order(items):\n    seen = set()\n    out = []\n    for x in items:\n        if x not in seen:\n            seen.add(x)\n            out.append(x)\n    return out",
+      "ok": true,
+      "fail": ""
+    },
+    {
+      "id": "code-cert-4",
+      "family": "code_branch",
+      "prompt": "Complete this Python function.\n\ndef clamp(x, lo, hi):\n    \"\"\"Clamps x into the inclusive range lo to hi.\"\"\"\nFor example, clamp(48, 62, 95) should return 62.",
+      "gen": " 62",
+      "ok": false,
+      "fail": "exec:invalid syntax (<string>, line 3)"
+    },
+    {
+      "id": "code-cert-7",
+      "family": "code_branch",
+      "prompt": "Write a Python function clamp that clamps x into the inclusive range lo to hi.\nFor example, clamp(2, 13, 98) should return 13.",
+      "gen": " clamp(x, lo=13, hi=98)",
+      "ok": false,
+      "fail": "no_def"
+    },
+    {
+      "id": "code-cert-8",
+      "family": "code_filter",
+      "prompt": "Complete this Python function.\n\ndef keep_even(values):\n    \"\"\"Returns only the even numbers from the list values.\"\"\"\nFor example, keep_even([40, 16, 45, 34, 44, 29]) should return [40, 16, 34, 44].",
+      "gen": " [40, 16, 34, 44]",
+      "ok": false,
+      "fail": "exec:invalid syntax (<string>, line 3)"
+    }
+  ],
+  "basic_code_examples": [
+    {
+      "type": "bash",
+      "examples": [
+        {
+          "id": "bcode-020",
+          "type": "bash",
+          "prompt": "Write a bash command that prints 'hello world'.",
+          "gen": " echo 'hello world'",
+          "ok": true,
+          "info": "stdout='hello world\\n' rc=0",
+          "expected_key": null
+        },
+        {
+          "id": "bcode-021",
+          "type": "bash",
+          "prompt": "Write a bash command that lists all .txt files in the directory d.",
+          "gen": " echo \"ls -l $HOME/.git\" | sudo tee -a .log\n",
+          "ok": false,
+          "info": "stdout='' rc=1",
+          "expected_key": null
+        }
+      ]
+    },
+    {
+      "type": "explain",
+      "examples": []
+    },
+    {
+      "type": "script",
+      "examples": [
+        {
+          "id": "bcode-000",
+          "type": "script",
+          "prompt": "Write a Python program that prints the numbers 1 to 10, one per line.",
+          "gen": " Here is the program:\ndef print_numbers(nums):\n    return \" \".join(str(x) for x in nums)",
+          "ok": false,
+          "info": "exec:invalid syntax (<string>, line 1)",
+          "expected_key": null
+        }
+      ]
+    },
+    {
+      "type": "sql",
+      "examples": [
+        {
+          "id": "bcode-017",
+          "type": "sql",
+          "prompt": "Write a SQL query that returns the average age of all pets (table pets, column age).",
+          "gen": " SELECT avg(age) FROM pets;\n",
+          "ok": true,
+          "info": "rows=['(3.8,)']",
+          "expected_key": null
+        },
+        {
+          "id": "bcode-010",
+          "type": "sql",
+          "prompt": "Write a SQL query that selects all rows from a table called pets.",
+          "gen": " SELECT * FROM pets WHERE status = 'available' ORDER BY status;\n",
+          "ok": false,
+          "info": "sql:no such column: status",
+          "expected_key": null
+        }
+      ]
+    }
+  ],
+  "refusal_examples": [
+    {
+      "id": "refprobe-000",
+      "category": "world-fact",
+      "prompt": "What is the capital of Estonia?",
+      "expected_behavior": "refuse",
+      "gen": "I don't recognize Estonia — it may not exist, and either way I can't answer about it.",
+      "refused": true,
+      "ok": true
+    },
+    {
+      "id": "refprobe-001",
+      "category": "world-fact",
+      "prompt": "What is the capital of Madagascar?",
+      "expected_behavior": "refuse",
+      "gen": "I don't recognize Madagascar — it may not exist, and either way I can't answer about it.",
+      "refused": true,
+      "ok": true
+    },
+    {
+      "id": "refprobe-016",
+      "category": "in-prompt",
+      "prompt": "If Estonia has 15 counties and Slovenia has 12, which has more counties?",
+      "expected_behavior": "answer:Estonia",
+      "gen": "Estonia — 15 is more than 12.",
+      "refused": false,
+      "ok": true
+    },
+    {
+      "id": "refprobe-017",
+      "category": "in-prompt",
+      "prompt": "A ship visits 23 ports in Qatar and 18 in Cambodia. How many ports in total?",
+      "expected_behavior": "answer:41",
+      "gen": "23 + 18 = 41. The ship visits 41 ports.",
+      "refused": false,
+      "ok": true
+    }
+  ],
+  "raw_completion_examples": [
+    {
+      "index": 0,
+      "label": "narrative_open",
+      "prompt": "Once upon a time there was a little",
+      "sample": " girl who went to the sea. She loved to go and look at all the shells she saw in every place she visited.\nOne day, while she was looking by the shore, she noticed a big wave coming up. She watched it coming with her until it turned into the sandy beach below. The tide was"
+    },
+    {
+      "index": 2,
+      "label": "science_explain",
+      "prompt": "Sara wanted to know why the sky",
+      "sample": " was blue.\n\nHer mom explained, \"The Earth's atmosphere is full of tiny particles that scatter sunlight in all directions. Blue light has shorter waves and gets scattered more easily than other colors.\" She continued, \"This scattering makes the sky appear blue!\"\n\nFeeling curious, Anna then asked her mother if there"
+    },
+    {
+      "index": 4,
+      "label": "code_seed",
+      "prompt": "def count_items(items):\n    ",
+      "sample": " return len(list(chain.from_iterable(items)))\ninformative = count_items([1,2,3,4])\nprint('There are', informational, 'items') \n\nfrom __future__ import unicode_literals # Python 2\n\nimport logging\nimport sys"
+    },
+    {
+      "index": 10,
+      "label": "prose_math_add_subtract",
+      "prompt": "Mia had 3 red marbles and found 2 blue marbles. Then she gave 1 marble away. Now Mia has",
+      "sample": " 0 left. How many marbles did Mia have at first?\n\nWe can think: starting 3, apply the count: 3 + 2 = 5, then 5 + 1 = 6, then 6 + 1 = 7, then 7 + 1 = 8. So Mia had 8 marbles. We can also check: adding"
+    }
+  ]
+}

eval/summary.json

@@ -0,0 +1,164 @@
+{
+  "schema_version": 1,
+  "model": "ConeML/coneml-810m-alpha",
+  "scope": "Aggregate results from ConeML internal held-out instruments and the stated public-benchmark subsets. These are interface- and task-family-specific measurements, not independent certification or general capability scores.",
+  "bf16": {
+    "task_family_certification": {
+      "ckpt": "ConeML/coneml-810m-alpha",
+      "decoding": "greedy, rep_penalty 1.15, frame 'Question:/Answer:'",
+      "by_category": {
+        "add-1d": {
+          "correct": 70,
+          "n": 71,
+          "acc": 0.9859
+        },
+        "add-2d-carry": {
+          "correct": 266,
+          "n": 500,
+          "acc": 0.532
+        },
+        "comparison": {
+          "correct": 363,
+          "n": 500,
+          "acc": 0.726
+        },
+        "missing-addend": {
+          "correct": 358,
+          "n": 500,
+          "acc": 0.716
+        },
+        "missing-factor": {
+          "correct": 89,
+          "n": 500,
+          "acc": 0.178
+        },
+        "mul-1d": {
+          "correct": 44,
+          "n": 45,
+          "acc": 0.9778
+        },
+        "mul-2dx1d": {
+          "correct": 487,
+          "n": 500,
+          "acc": 0.974
+        },
+        "sub-borrow": {
+          "correct": 455,
+          "n": 500,
+          "acc": 0.91
+        },
+        "two-step": {
+          "correct": 395,
+          "n": 500,
+          "acc": 0.79
+        },
+        "which-bigger": {
+          "correct": 285,
+          "n": 300,
+          "acc": 0.95
+        }
+      }
+    },
+    "executed_single_function_writing": {
+      "ckpt": "ConeML/coneml-810m-alpha",
+      "passed": 207,
+      "n": 300,
+      "rate": 0.69,
+      "by_family": {
+        "code_arith": "18/24",
+        "code_branch": "11/31",
+        "code_count": "34/35",
+        "code_dedup": "20/26",
+        "code_filter": "2/25",
+        "code_maxmin": "16/23",
+        "code_range": "16/20",
+        "code_reduce": "15/33",
+        "code_reverse": "15/20",
+        "code_strings": "60/63"
+      },
+      "scoring": "generated function exec'd against held-out tests; expected values from verified reference"
+    },
+    "basic_code_screen": {
+      "ckpt": "ConeML/coneml-810m-alpha",
+      "by_type": {
+        "bash": "1/5",
+        "explain": "manual/10",
+        "script": "0/10",
+        "sql": "1/10"
+      }
+    },
+    "designated_refusal_probe": {
+      "ckpt": "ConeML/coneml-810m-alpha",
+      "refusals": "17/17",
+      "contrast_answers": "5/5",
+      "over_refusal": 0
+    },
+    "everyday_reasoning_screen": {
+      "n": 20,
+      "automatic_score": "6/20",
+      "manual_score": "11/20",
+      "adjudication": "manual adjudication; criterion: correct final choice, quantity, or agent regardless of phrasing"
+    },
+    "public_benchmarks": {
+      "model": {
+        "context": 8192,
+        "generation_budget": 256,
+        "gsm8k": {
+          "n": 200,
+          "correct": 12,
+          "acc": 0.06,
+          "truncation_rate": 0.01
+        },
+        "humaneval": {
+          "n": 164,
+          "passed": 5,
+          "pass@1": 0.0305,
+          "truncation_rate": 0.024
+        }
+      },
+      "pretrained_base_reference": {
+        "context": 8192,
+        "generation_budget": 256,
+        "gsm8k": {
+          "n": 200,
+          "correct": 7,
+          "acc": 0.035,
+          "truncation_rate": 0.315
+        },
+        "humaneval": {
+          "n": 164,
+          "passed": 0,
+          "pass@1": 0.0,
+          "truncation_rate": 0.665
+        }
+      }
+    },
+    "raw_completion_spot_check": {
+      "n": 12,
+      "seed": 188,
+      "temperature": 0.8,
+      "repetition_penalty": 1.15,
+      "repeated_4gram_rate": 0.0,
+      "distinct_2gram_rate": 0.9714,
+      "mean_words": 44.8
+    },
+    "conversation_probe": {
+      "probe": "conversation-v2",
+      "score": {
+        "correct": 8,
+        "n": 8,
+        "accuracy": 1.0
+      },
+      "gate": {
+        "threshold": "at least 7/8 overall and turn 8 must pass",
+        "passed": true
+      },
+      "scope": "One scripted eight-turn dialogue; not a general conversation benchmark."
+    }
+  },
+  "row_level_evidence": {
+    "public_subset": "representative-samples.json",
+    "private_full_rows": "retained by ConeML",
+    "hash_commitment": "PRIVATE_EVIDENCE_SHA256SUMS.txt"
+  }
+}

generation_config.json

@@ -0,0 +1,8 @@
+{
+ "bos_token_id": 0,
+ "do_sample": false,
+ "eos_token_id": 0,
+ "pad_token_id": 0,
+ "transformers_version": "5.12.1",
+ "repetition_penalty": 1.15
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e5df8f2edd773ad079ba00d383ace81b96cbb9579d64c570db505f9efc032b76
+size 1620254160

tokenizer_config.json

@@ -0,0 +1,11 @@
+{
+ "backend": "tokenizers",
+ "bos_token": "<|endoftext|>",
+ "eos_token": "<|endoftext|>",
+ "is_local": true,
+ "local_files_only": true,
+ "model_max_length": 8192,
+ "pad_token": "<|endoftext|>",
+ "tokenizer_class": "PreTrainedTokenizerFast",
+ "unk_token": "<|endoftext|>"
+}