RWK-SMI-20260822

Provenance

Training mixture

The locked corpus artifact contains 93,235,868 assistant target tokens across 134,295 rows. This full-sft stage selected buckets short, medium: 74,229,330 target tokens across 132,586 rows.

The values below are copied from smi_corpus_manifest.json; they are not estimates.

SMI usage and protocol

The tokenizer assigns these atomic, append-only IDs: =65536, =65537, =65538, =65539, =65540, =65541, =65542, =65543, =65544, =65545. Compile trusted message structure to token IDs with an SMI-compatible compiler; do not interpolate untrusted payload text into structural markers. Runtime turns end with `` (ID 65545). Generation stops on either ID 0 or ID 65545.

The preserved chat_template.jinja, smi_token_ids.json, and tokenizer artifacts are the training-time protocol contract. Consumers should hash-pin this repository and use trust_remote_code=True for the bundled model implementation.

Training configuration

Evaluation

Values are copied from the closed-schema smi_evaluation.json v2. Main cases SHA-256: aca1b98413377a3bffa6fed28d024777e34195abfb3ef9e11abeca08433739a7. Multi-turn cases SHA-256: d5a407b61e700805ab1a58eb7cd830bf1f5b395c1355416f580316e85033d9ce.

Top-1 parity: 16 / 16.

Loading

Install the supported runtime first:

python -m pip install "transformers>=5.3,=1.5,<2"

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedConfig

model_id = "aabbdev/RWKV7-1.5B-SMI-20260822"
tokenizer = AutoTokenizer.from_pretrained(model_id, config=PreTrainedConfig())
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype=torch.bfloat16,
)

OpenAI-compatible serving

python -m pip install -r inference/requirements.txt
python inference/serve.py --host 127.0.0.1 --port 8000

The tokenizer response template maps SMI thinking, output, and actions to reasoning_content, content, and OpenAI tool_calls. Tool observations are sent back as standard role="tool" messages with the returned tool_call_id. Continuous batching is intentionally rejected because RWKV uses recurrent state, not a paged KV cache. The launcher requires transformers[serving]>=5.15,<6; direct model loading remains compatible with Transformers 5.3+.

Known limitations

• SMI structural-token discipline is a serialization boundary, not a complete security sandbox or a guarantee that generated tool calls are safe to execute.
• Fine-tuning and the reported benchmark do not establish broad factuality, safety, multilingual quality, or production suitability.
• Recurrent-cache rollback for assisted/speculative decoding is unsupported.
• The optional optimized runtime has hardware-, dtype-, and shape-specific limits and falls back to eager PyTorch outside validated boundaries.
• No evaluation values are inferred: when smi_evaluation.json is absent, this card makes no quantitative training-final or benchmark claim.

License and notices

The derived weight-license identifier is reported as apache-2.0 from release metadata; other means that this publisher makes no specific weight-license claim. The generated remote code and inference bundle are distributed under Apache-2.0; see LICENSE and NOTICE.