初始化项目，由ModelHub XC社区提供模型

Model: ConeML/coneml-810m-alpha Source: Original Platform
2026-10-16 09:24:22 +08:00
commit c6748fe1d9
23 changed files with 164329 additions and 0 deletions
--- a/README.md
+++ b/README.md
@@ -0,0 +1,271 @@
+---
+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. Peer comparison and efficiency
+
+The pair was tested against five post-trained peers on the same reduced
+diagnostic screen, through each model's native instruction interface,
+with greedy decoding, repetition penalty 1.15, and matched short-answer
+budgets.
+
+| model | parameters | mixed arithmetic (n=585) | executed functions (n=100) | transitive names d1/d3/d5 (n=32 each) | designated refusals (n=17) |
+|---|---:|---:|---:|---:|---:|
+| ConeML 810M Alpha | 0.81B | 421 (72.0%) | 83 | 23/15/15 | **13** |
+| ConeML 810M Alpha-Arithmetic | 0.81B | **442 (75.6%)** | 35 | 26/17/16 | 11 |
+| Qwen3.5 | 0.8B | 154 (26.3%) | 93 | 23/22/10 | 0 |
+| Qwen3 | 0.6B | 223 (38.1%) | **98** | 18/10/9 | 1 |
+| Llama 3.2 Instruct | 1.24B | 343 (58.6%) | 79 | 16/7/5 | 1 |
+| TinyLlama Chat | 1.1B | 113 (19.3%) | 51 | 8/18/22 | 0 |
+| SmolLM2 Instruct | 1.7B | 380 (65.0%) | 96 | 16/6/5 | 1 |
+
+This is not a general leaderboard: the task families match ConeML's
+trained surfaces. On this screen, the ConeML pair led mixed arithmetic;
+ConeML 810M Alpha exceeded Llama 3.2 and TinyLlama on function writing
+but trailed Qwen3.5, Qwen3, and SmolLM2. All models recorded zero
+over-refusals on five in-scope contrasts.
+
+On the question-form name-chain depth screen, ConeML 810M
+Alpha-Arithmetic recorded 26/32, 17/32, and 16/32 at depths 1, 3, and 5;
+ConeML 810M Alpha recorded 23/32, 15/32, and 15/32. The arithmetic
+variant exceeded Qwen3 and Llama 3.2 at all three shown depths and
+Qwen3.5 at depths 1 and 5, while Qwen3.5 led it at depth 3 and TinyLlama
+led the group at depth 5. Entity-chain controls were mixed and are
+reported in the full table. Chance is 1/(depth+1); this is an
+exact-selection surface test, not a claim of general reasoning.
+
+Qwen3.5 with thinking enabled reached 451/585 (77.1%), compared with
+ConeML 810M Alpha-Arithmetic's 442/585 (75.6%), while using at least 17.6 times the
+generated-token budget per item and approximately 24 times the recorded
+wall time. Runtime ratios are implementation-specific.
+
+ConeML's selected base consumed 12.32B token positions—15.2 tokens per
+parameter and approximately 5.99e19 training FLOPs under the `6ND`
+convention. Pretraining took approximately 11 days on one RTX 5090.
+Assuming, rather than claiming to have metered, a 0.70 kW average wall
+draw gives 184.8 kWh; at CHF 0.14–0.30/kWh, that is approximately
+CHF 26–55 of marginal pretraining electricity. This excludes hardware,
+labor, SFT, evaluation, conversion, and emissions accounting.
+
+The complete category table, peer wins, adverse base result, evaluated
+revisions, assumptions, and frozen evidence commitments are in
+[`eval/PEER_COMPARISON.md`](eval/PEER_COMPARISON.md).
+
+## 3. 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.
+
+## 4. 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.
+
+## 5. 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.
+
+For local GGUF inference:
+
+```bash
+llama-cli \
+  -m coneml-810m-alpha-Q8_0.gguf \
+  -p $'Question: What is 7 + 8?\nAnswer:' \
+  -n 32 --repeat-penalty 1.15 --no-conversation --single-turn
+```
+
+For Ollama, download `Modelfile` beside the Q8_0 file, then run:
+
+```bash
+ollama create coneml-810m-alpha -f Modelfile
+ollama run coneml-810m-alpha "What is 7 + 8?"
+```
+
+## 6. Loading with Transformers
+
+```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.
+
+## 7. 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.
+
+## 8. 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.
+
+## 9. 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.
+- `peer-comparison-summary.json`, `PEER_COMPARISON.md`, and
+  `PEER_EVIDENCE_SHA256SUMS.txt`: the peer results, interpretation, and
+  commitments to the retained peer-generation evidence.
+
+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.
+
+## 10. Research license and commercial work
+
+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, model-engineering, or licensing enquiries,
+contact [contact@coneml.com](mailto:contact@coneml.com) or visit
+[ConeML for Organizations](https://coneml.com/organizations/). Commercial availability does not
+imply that this alpha checkpoint is suitable for an unvalidated
+production workflow.