Add Vinci scaffold, contract, and CPU smoke artifacts

README.md

@@ -96,4 +96,4 @@ comparisons.
 - Voyage or the current production provider remains default until Vinci matches
   or beats the baseline without latency regression.
 
-Generated: `2026-05-02T02:48:43+00:00`
+Generated: `2026-05-02T02:51:25+00:00`

artifacts/hf_job_request.json

@@ -31,7 +31,7 @@
       "HUB_MODEL_ID": "Cilow/Vinci"
     },
     "flavor": "a10g-large",
-    "script": "# /// script\n# dependencies = [\n#   \"sentence-transformers>=3.0\",\n#   \"datasets>=2.20\",\n#   \"torch>=2.4\",\n#   \"accelerate>=0.33\",\n#   \"transformers>=4.44\",\n#   \"peft>=0.12\",\n#   \"huggingface_hub>=0.24\",\n# ]\n# ///\n\nimport json\nimport os\nfrom pathlib import Path\n\nfrom datasets import Dataset\nfrom huggingface_hub import hf_hub_download\nfrom sentence_transformers import (\n    SentenceTransformer,\n    SentenceTransformerTrainer,\n    SentenceTransformerTrainingArguments,\n    losses,\n)\n\nDATASET_REPO = os.environ[\"DATASET_REPO\"]\nHUB_MODEL_ID = os.environ[\"HUB_MODEL_ID\"]\nBASE_MODEL = os.environ.get(\"BASE_MODEL\", \"BAAI/bge-m3\")\n\n\ndef read_jsonl(filename):\n    path = Path(hf_hub_download(repo_id=DATASET_REPO, filename=filename, repo_type=\"dataset\"))\n    rows = []\n    with path.open(\"r\", encoding=\"utf-8\") as fh:\n        for line in fh:\n            line = line.strip()\n            if line:\n                rows.append(json.loads(line))\n    return rows\n\n\ndef to_training_rows(rows):\n    return [\n        {\n            \"anchor\": \"Represent this sentence for searching relevant passages: \" + row[\"query\"],\n            \"positive\": row[\"positive_text\"],\n            \"negative\": row[\"negative_text\"],\n        }\n        for row in rows\n    ]\n\n\ntrain_rows = read_jsonl(\"train_triplets.jsonl\")\ndev_rows = read_jsonl(\"dev_triplets.jsonl\")\ntrain_dataset = Dataset.from_list(to_training_rows(train_rows))\neval_dataset = Dataset.from_list(to_training_rows(dev_rows)) if dev_rows else None\n\nmodel = SentenceTransformer(BASE_MODEL)\n\nfrom peft import LoraConfig, TaskType\nmodel.add_adapter(\n    LoraConfig(\n        task_type=TaskType.FEATURE_EXTRACTION,\n        r=16,\n        lora_alpha=32,\n        lora_dropout=0.05,\n        bias=\"none\",\n    )\n)\n\ncached_loss = getattr(losses, \"CachedMultipleNegativesRankingLoss\", None)\nbase_loss = cached_loss(model=model) if cached_loss is not None else losses.MultipleNegativesRankingLoss(model=model)\ntrain_loss = losses.MatryoshkaLoss(model=model, loss=base_loss, matryoshka_dims=[1024, 256])\n\nargs = SentenceTransformerTrainingArguments(\n    output_dir=\"vinci-v0-output\",\n    num_train_epochs=1,\n    per_device_train_batch_size=16,\n    per_device_eval_batch_size=16,\n    gradient_accumulation_steps=4,\n    learning_rate=2e-05,\n    seed=42,\n    data_seed=42,\n    eval_strategy=\"epoch\" if eval_dataset is not None else \"no\",\n    save_strategy=\"epoch\",\n    save_total_limit=2,\n    logging_steps=10,\n    report_to=\"none\",\n    run_name=\"vinci-v0-gpu_16gb_lora\",\n    push_to_hub=True,\n    hub_model_id=HUB_MODEL_ID,\n    hub_strategy=\"end\",\n    max_steps=20,\n)\n\ntrainer = SentenceTransformerTrainer(\n    model=model,\n    args=args,\n    train_dataset=train_dataset,\n    eval_dataset=eval_dataset,\n    loss=train_loss,\n)\ntrainer.train()\nmodel.save_pretrained(\"vinci-v0-output\")\nmodel.push_to_hub(HUB_MODEL_ID)\n",
+    "script": "# /// script\n# dependencies = [\n#   \"sentence-transformers>=3.0\",\n#   \"datasets>=2.20\",\n#   \"torch>=2.4\",\n#   \"accelerate>=0.33\",\n#   \"transformers>=4.44\",\n#   \"peft>=0.12\",\n#   \"huggingface_hub>=0.24\",\n# ]\n# ///\n\nimport json\nimport os\nfrom pathlib import Path\n\nfrom datasets import Dataset\nfrom huggingface_hub import hf_hub_download\nfrom sentence_transformers import (\n    SentenceTransformer,\n    SentenceTransformerTrainer,\n    SentenceTransformerTrainingArguments,\n    losses,\n)\n\nDATASET_REPO = os.environ[\"DATASET_REPO\"]\nHUB_MODEL_ID = os.environ[\"HUB_MODEL_ID\"]\nBASE_MODEL = os.environ.get(\"BASE_MODEL\", \"BAAI/bge-m3\")\n\n\ndef read_jsonl(filename):\n    path = Path(hf_hub_download(repo_id=DATASET_REPO, filename=filename, repo_type=\"dataset\"))\n    rows = []\n    with path.open(\"r\", encoding=\"utf-8\") as fh:\n        for line in fh:\n            line = line.strip()\n            if line:\n                rows.append(json.loads(line))\n    return rows\n\n\ndef to_training_rows(rows):\n    return [\n        {\n            \"anchor\": \"Represent this sentence for searching relevant passages: \" + row[\"query\"],\n            \"positive\": row[\"positive_text\"],\n            \"negative\": row[\"negative_text\"],\n        }\n        for row in rows\n    ]\n\n\ntrain_rows = read_jsonl(\"train_triplets.jsonl\")\ndev_rows = read_jsonl(\"dev_triplets.jsonl\")\ntrain_dataset = Dataset.from_list(to_training_rows(train_rows))\neval_dataset = Dataset.from_list(to_training_rows(dev_rows)) if dev_rows else None\n\nmodel = SentenceTransformer(BASE_MODEL)\n\nfrom peft import LoraConfig, TaskType\nmodel.add_adapter(\n    LoraConfig(\n        task_type=TaskType.FEATURE_EXTRACTION,\n        r=16,\n        lora_alpha=32,\n        lora_dropout=0.05,\n        bias=\"none\",\n    )\n)\n\ncached_loss = getattr(losses, \"CachedMultipleNegativesRankingLoss\", None)\nbase_loss = cached_loss(model=model) if cached_loss is not None else losses.MultipleNegativesRankingLoss(model=model)\ntrain_loss = losses.MatryoshkaLoss(model=model, loss=base_loss, matryoshka_dims=[1024, 256])\n\nargs = SentenceTransformerTrainingArguments(\n    output_dir=\"vinci-v0-output\",\n    num_train_epochs=1,\n    per_device_train_batch_size=16,\n    per_device_eval_batch_size=16,\n    gradient_accumulation_steps=4,\n    learning_rate=2e-05,\n    seed=42,\n    data_seed=42,\n    eval_strategy=\"epoch\" if eval_dataset is not None else \"no\",\n    save_strategy=\"epoch\",\n    save_total_limit=2,\n    logging_steps=10,\n    report_to=\"none\",\n    run_name=\"vinci-v0-gpu_16gb_lora\",\n    push_to_hub=True,\n    hub_model_id=HUB_MODEL_ID,\n    hub_strategy=\"end\",\n    max_steps=20,\n)\n\ntrainer = SentenceTransformerTrainer(\n    model=model,\n    args=args,\n    train_dataset=train_dataset,\n    eval_dataset=eval_dataset,\n    loss=train_loss,\n)\ntrainer.train()\nmodel.save_pretrained(\"vinci-v0-output\")\nmodel.push_to_hub(HUB_MODEL_ID, exist_ok=True)\n",
     "secrets": {
       "HF_TOKEN": "$HF_TOKEN"
     },

vinci_model_contract.json

@@ -2,7 +2,7 @@
   "base_model": "BAAI/bge-m3",
   "dataset_repo": "Cilow/Vinci-Evals",
   "full_dim": 1024,
-  "generated_at": "2026-05-02T02:48:43+00:00",
+  "generated_at": "2026-05-02T02:51:25+00:00",
   "legacy_model_ids": [
     "cilow/vinci-v0",
     "cilow/cilow-embed-v0"