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Direct-weight chat: versioned offline research
Pre-release
Direct-weight chat: initial public research release
Ten preserved checkpoints, including failed candidates. Each extracts to exactly
250,000,000 float32 parameters / 1,000,000,000 weight bytes. No training,
pretrained tensors, downloaded datasets or internet access were used during
construction/inference. GitHub access is used to publish this experiment.
Current model: v7a. This is a sparse, explicitly compiled causal transformer,
not a useful general-purpose conversational model.
| Version | Legacy /185 | Graph /79 | Arithmetic /120 |
|---|---|---|---|
| v1 | 131 | not run | not run |
| v2 | 177 | not run | not run |
| v3 | 182 | not run | not run |
| v4 | 182 | not run | not run |
| v5 | 185 | 12 | not run |
| v6a, failed scratch allocation | 1 | 8 | not run |
| v6b, partial graph candidate | 182 | 68 | not run |
| v6c, bounded graph traversal | 185 | 74 | 22 |
| v7a, signed subtraction | 185 | 74 | 114 |
Fresh combinations generated after candidate freeze reproduced graph 74/79 and
arithmetic 114/120 on CUDA and CPU. The generators and grammar were already
known; this is not an independent general-intelligence benchmark. Every failed
stress case is retained. The original selector v0 uses canned responses and is
included only as a rejected historical prototype, with its separate test evidence.
v7a follows up to three unfamiliar class links for tested property questions and
adds/subtracts two nonnegative integers at most 999, emitting signed results.
It still fails open-ended conversation, code generation, creative writing,
negation, longer graph chains and general mathematics. Negative operands and
chained expressions can produce incorrect numbers. Only 717,554 parameters
(0.287%) are nonzero; small ZIP sizes reflect this sparsity, not 1 GB of knowledge.
Use
Download the source and autoregressive-v7a.zip; extract both into the same
repository directory. Use an existing offline Python environment with PyTorch:
python -m chat1gb_autoregressive.chat --device auto
To rebuild without downloads in an environment already containing NumPy:
python -m tools.build_version v7a --output build/reproduced
The archived v7a compiler was rebuilt and reproduced the exact weight and config
hashes. SHA256SUMS.txt verifies each ZIP; each model manifest verifies the raw
weights. All ten ZIPs were streamed through CRC and decompressed SHA-256 checks.
Historical archives extract under versions/autoregressive_VERSION/model and
can be selected with --model-dir. The selector archive uses chat1gb/model.
- Full version results and exact outputs
- Research log
- Capabilities and limitations
- Circuit architecture
Current raw weight SHA-256:
85a6b709cead89d261a1b1d412ad8e64bac19d4e12b9f9ec6bc947fff1869c2d.
Code and experiment artifacts are MIT licensed. This is an experimental prerelease.