README.md

metadata

pretty_name: Deterministic Random Models
license: other
size_categories:
  - n<1K
tags:
  - llama
  - gemma4
  - qwen3
  - smollm3
  - transformers
  - safetensors
  - gguf
  - synthetic
  - conformance
  - compatibility-testing

Deterministic Random Models

This dataset contains ten small, deterministic language-model fixtures for model-format, loader, inference, compatibility, and conformance testing. They are not trained models and must not be used for language-model quality evaluation.

All weights are synthetic and deterministically generated. No original model checkpoint weights are included.

Cases

Case	Architecture	Parameters	Hugging Face	GGUF	Notable feature
tinyllama-chat	Llama	303,744	F32	Q4_K_M	GQA, query/KV ratio 8
smollm2-instruct	Llama	46,320	F32	Q4_K_M	GQA, query/KV ratio 3
mobilellama-chat	Llama	9,296	F32	Q4_K_M	MHA
minicpm5	Llama	1,409,664	F32	Q4_K_M	explicit head dimension, multiple EOS IDs
deepseek-coder	Llama	9,296	F32	Q4_K_M	linear RoPE scaling
hermes3-llama31	Llama	86,336	F32	Q4_K_M	Llama 3 RoPE scaling
livekit-turn-detector	Llama	132,336	F32	Q4_K_M	explicit head dimension, GQA
gemma4-random-model	Gemma 4	1,519,168	BF16	Q4_0	five-local/one-global attention schedule
qwen3-random-model	Qwen 3	508,800	BF16	Q4_0	wide Q projection and Q/K head norms
smollm3-random-model	SmolLM3	4,917,504	BF16	Q4_0	three-RoPE/one-no-RoPE layer schedule

The seven Llama cases are derived from real Hugging Face configuration files by a preservation-first shrinker. Gemma 4, Qwen 3, and SmolLM3 retain architecture-specific reduced geometries that preserve important ratios, tensor inventories, and layer schedules observed in locally downloaded upstream GGUF models. Published case names use random-model rather than tiny-model to avoid collision with a separately maintained TinyModel collection.

Formats and layout

The Llama cases retain the original dataset layout:

<llama-case>/
|-- package/model.safetensors       # canonical F32 weights
|-- gguf/model-Q4_K_M.gguf
|-- tokenizer/
|-- reference/outputs.safetensors
|-- inputs.safetensors
|-- case.json
|-- provenance.json
|-- source-config.json
|-- shrunk-config.json
|-- config-diff.json
`-- validation.json

The architecture-specific cases use:

<random-model-case>/
|-- hf-bf16/
|   |-- config.json
|   |-- model.safetensors
|   |-- tokenizer.json
|   `-- tokenizer_config.json
|-- gguf-q4_0/
|   |-- <case>-Q4_0.gguf
|   `-- quantize.log
|-- reference/
|   |-- inputs.json
|   |-- hf-outputs.safetensors
|   `-- gguf-native.json
|-- CONFIG_DECISION.md
`-- metadata.json

manifest.json is the machine-readable index of all ten model packages and their SHA-256 hashes and sizes.

Synthetic weights and tokenizers

Weights use the tlfloat::LCG64 recurrence with multiplier 6364136223846793005, increment 1442695040888963407, and ten warm-up steps. Each case records its seed and provenance.

The reduced models use deterministic 128-token auxiliary vocabularies. These tokenizers cover token IDs 0..127 and preserve each case's special-token semantics, but they do not reproduce the linguistic behavior of the original tokenizer. Explicit token IDs are the primary numerical-test interface.

GGUF generation and validation

GGUF files were generated with upstream ggml-org/llama.cpp commit 40b740ad05c531b9d57aca6698c3ed553a9e784c.

Every retained GGUF was loaded through that revision and exercised with direct token IDs for prefill, cached decode, logit extraction, finite-value checks, and repeated-execution checks. The effective EOG token set was checked against the model EOS semantics. Per-case metadata records the actual tensor-type histogram, hashes, commands, and informational comparison with the corresponding Transformers reference.

Q4_K_M and Q4_0 are lossy formats. Their logits are not required to equal the F32 or BF16 reference exactly.

Reproducibility and scope

The Hugging Face weights, configs, and GGUF outputs for Gemma 4, Qwen 3, and SmolLM3 were independently regenerated and found byte-identical. The Llama cases retain their source revisions, source-config hashes, shrink decisions, and generation provenance in each case directory.

This dataset is not a pretrained-model collection, a model-quality benchmark, or a reproduction of upstream weights or tokenizers. Source-derived configuration and metadata files may remain subject to terms of their respective upstream repositories; consult their recorded provenance before redistribution.

See REPORT.md, GGUF_Q4_K_M_REPORT.json, and ARCHITECTURE_RANDOM_MODELS_REPORT.json for collection-level summaries.