Buckets:
| # Command-line tools | |
| The build puts the tools in `out/bin` (macOS) or `build/bin` (Windows, Linux). | |
| ## Run | |
| | Tool | Use | | |
| |---|---| | |
| | `nslm-chat` | Run one prompt. Read [Run and serve](../guides/run-and-serve.md) | | |
| | `nslm-serve` | OpenAI-compatible HTTP server | | |
| ## Measure | |
| | Tool | Use | | |
| |---|---| | |
| | `nslm-mova-bench` | Prompt and decode speed, memory and machine state | | |
| | `nslm-mova-score` | Logits for KLD against a reference | | |
| | `nslm-mova-gen` | Greedy and sampled outputs for a set of prompts | | |
| | `nslm-mova-plcheck` | Prompt-lookup decode against plain decode. The two must be identical | | |
| | `nslm-mova-kbench` | Speed of one kernel at the MoVA shapes. There is one program for each GPU API. The Metal program also selects Metal kernel variants | | |
| | `nslm-mova-refcheck` | The GPU engine against a C reference forward on the CPU (KL, argmax, router choices) | | |
| | `nslm-mova-routes` | Router choices for a prompt and a generation, for expert-cache studies | | |
| | `nslm-qwen4-gen` | Greedy generation of one prompt through the Qwen3.8-Flash-Next CUDA engine, with prompt and decode speed | | |
| | `nslm-qwen4-kld` | KL divergence, top-1 agreement and perplexity of a Qwen3.8-Flash-Next model against a reference, on one text | | |
| ## Make models | |
| | Tool | Use | | |
| |---|---| | |
| | `nslm-moe` | Seed search for experts (`--scope gud`) and MLA projections (`--scope mla`). Qwen3.8-Flash-Next experts take `--imatrix` | | |
| | `nslm-mova-mlacapture` | Activation statistics and `X^T X` of the MLA projection inputs | | |
| | `nslm-mova-pack` | Write a model folder | | |
| | `nslm-qwen4-pack` | Write a Qwen3.8-Flash-Next MLX folder with seed experts, from an MLX base folder | | |
| | `nslm-qwen4-emulate` | Write seed experts into Unsloth's Q8_0 GGUF shards in place, so that llama.cpp runs them. A map takes each expert from one of several packs | | |
| Read [Make a model folder](../guides/make-a-model.md). | |
| ## Python tools (`tools/`) | |
| | Tool | Use | | |
| |---|---| | |
| | `nanoseedlm_k2.py` | The MLX loader for K2-Horizon | | |
| | `nanoseedlm_qwen4.py` | The MLX loader for Qwen3.8-Flash-Next (mlx-vlm) | | |
| | `nanoseedlm_seeds.py` | The seed kernels and modules that the MLX loaders share | | |
| | `qwen4_golden.py` | A tiny random Qwen3.8-Flash-Next model and its mlx-vlm logits, for `tests/test_qwen4_ref.c` | | |
| | `qwen4_cache_sim.py` | Replays a routing trace (`Q4E_ROUTES`) through expert cache policies: LRU, LFU, a hot set and the optimum | | |
| | `hf_cuda_test.sh` | Build and run CUDA tests on an HF Jobs GPU node | | |
| | `hf_qwen4_gen.sh` | Run a Qwen3.8-Flash-Next pack through `nslm-qwen4-gen` on an HF Jobs GPU node | | |
| | `hf_qwen4_kld.sh` | Score packs with `nslm-qwen4-kld` against a reference model on wikitext-2, on an HF Jobs GPU node | | |
| | `hf_llama_kld.sh` | Score Unsloth packs and emulated seed packs against BF16 in llama.cpp, with the BF16 PLE table | | |
| | `hf_qwen4_routes.sh` | Record routing traces of a pack on chat and code text, on an HF Jobs GPU node | | |
| | `qwen4_tier_map.py` | Write a per-expert tier map from routing traces (most used experts), at random, or by projection | | |
| | `mova_capture.py` | Activation statistics for the expert search, through MLX | | |
| | `mova_score.py` | Scores in MLX | | |
| | `mova_export.py` | Inputs for `nslm-mova-score`, `nslm-mova-gen`, `nslm-mova-bench` and route studies | | |
| | `tokenizer_golden.py`, `template_golden.py`, `mova_affine_golden.py` | Golden files for the tests | | |
| | `serve_bench.py` | Server speed. It waits for an idle GPU and records machine state | | |
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