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GeorgeandSigbjørn Skjæret
7d6f5d02bb
* model : add support for HrmTextForCausalLM (DFM Mimir 1B) HRM-Text runs two transformer stacks (low, high) in an alternating cycle over the same token stream. The low-cycle state z_l starts from a learned [n_embd] tensor and is broadcast over positions. - conversion: new writer for the fused gqkv projection (order gate,q,k,v) remapped to llama.cpp q/k/v plus a separate sigmoid gate tensor - loader: block_count = lps * h_cycles * (l_cycles + 1) cache slots aliasing 2*lps physical blocks via struct copies - graph: looped build with sigmoid-gated attention, SwiGLU FFN and parameterless RMS norms; learned embedding_scale applied in build_inp_embd - saver: pointer-deduplicated layer loop (looped archs alias tensors) - tests: hrm_text fixture (lps 1, h 2, l 3) in test-llama-archs Limitations: causal attention only - the upstream prefix-LM mode is not implemented (the prefix_lm GGUF key round-trips unused). The KV cache holds one entry per pass: 128 layers for Mimir 1B, i.e. 4x a same-width 32-layer model - about 3072 MiB at ctx 4096 in F16 (halves with q8_0 KV + FA). Every token runs all 128 block passes, so decode cost is roughly 4x a dense model of equal width (2.65 t/s BF16, 8-thread desktop CPU). Verified against the HF reference: identical argmax at 334/334 positions across 20 prompts (BF16 GGUF vs FP32 golden). q8_0 requant: 95.8% top-1, all remaining misses inside the HF top-5 (accumulated error over 128 sequential blocks). AI usage disclosure: YES Used GLM-5.3 for the majority of code AI-generated under my direction, all gates verified locally. All in all I could say that I have written less than 20% of the code and most of the heavy lifting has been done by the model. As such, this should be considered experimental. * Update conversion/hrm_text.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * Update src/llama-arch.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * convert : add gguf_writer methods for hrm_text metadata replace raw add_uint32/add_bool calls with dedicated GGUFWriter methods, following the add_embedding_scale pattern Assisted-by: GLM-5.3 * convert : map regular hrm_text tensors via tensor_mapping delegate unfused checkpoints to the base tensor mapping; training-style attn. names are renamed to self_attn. so the patterns match Assisted-by: GLM-5.3 * model : format hrm-text build_* calls as in other models one argument group per line, matching sibling model files Assisted-by: GLM-5.3 * llama : move hrm z_l_init table entries out of the nemotron group place the name and tensor-info entries with the other global input tensors Assisted-by: GLM-5.3 * convert : slim down hrm_text comments Assisted-by: GLM-5.3 * convert : build hrm_text block tensor names from the {bid} template The tensor map holds concrete per-block names, so format the template with the computed layer index before handing it to super(). * llama : name hrm metadata keys in their own hrm. namespace The four keys are arch-independent, unlike the arch-substituted Keys.LLM entries, so group them under Keys.HRM (like Keys.Split) and rename the llm_kv entries to LLM_KV_HRM_*. Only our own GGUFs carry the old hrm_text.* keys; they are regenerated. * Update src/llama-model-saver.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * llama : keep hrm metadata keys arch-substituted Per review: the GGUF keys stay "{arch}.h_cycles" style, so the Python members drop the LLM_KV_HRM_ prefix and keep arch templates; C++ keeps the LLM_KV_HRM_* enums. GGUF output is unchanged - existing files and HF uploads stay valid. * Update gguf-py/gguf/constants.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * Update src/llama-arch.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * Update src/llama-arch.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * convert : rename hrm writer methods to add_hrm_* Generic names like add_h_cycles/add_prefix_lm are too broad on the shared GGUFWriter; prefix them with hrm_ like the metadata keys. * model : fix meta-split lookup for archs with aliased cache slots Cache tensors of archs that alias physical blocks across looped slots (hrm_text, nanbeige with num_loops > 1) can reference block indices without weight tensor names. Take the output projection from the layer array instead of asserting; all other lookups are unchanged. * model : replicate hrm_text tensors on meta devices instead of splitting The aliased cache slots rotate split states differently from their physical weights, so the meta-split execution invariants (set_rows requires the cache state to match the token indices) cannot hold for any device count. Replicate all hrm_text tensors on every meta device instead; single-device and non-meta paths are unchanged. Assisted-by: Claude Sonnet --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>