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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>
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| .. | ||
| fusion | ||
| peg-parser | ||
| snapshots | ||
| .gitignore | ||
| CMakeLists.txt | ||
| gguf-model-data.cpp | ||
| gguf-model-data.h | ||
| test-alloc.cpp | ||
| test-arg-parser.cpp | ||
| test-autorelease.cpp | ||
| test-backend-ops.cpp | ||
| test-backend-sampler.cpp | ||
| test-barrier.cpp | ||
| test-batch-alloc.cpp | ||
| test-c.c | ||
| test-chat-analysis.cpp | ||
| test-chat-auto-parser.cpp | ||
| test-chat-peg-parser.cpp | ||
| test-chat-template.cpp | ||
| test-chat.cpp | ||
| test-col2im-1d.cpp | ||
| test-double-float.cpp | ||
| test-export-graph-ops.cpp | ||
| test-fusion.cpp | ||
| test-gbnf-validator.cpp | ||
| test-gguf-model-data.cpp | ||
| test-gguf.cpp | ||
| test-grammar-integration.cpp | ||
| test-grammar-llguidance.cpp | ||
| test-grammar-parser.cpp | ||
| test-jinja.cpp | ||
| test-json-schema-to-grammar.cpp | ||
| test-json-schema.cpp | ||
| test-llama-archs.cpp | ||
| test-llama-grammar.cpp | ||
| test-log.cpp | ||
| test-lora-conversion-inference.sh | ||
| test-model-load-cancel.cpp | ||
| test-model-resolution.cpp | ||
| test-mtmd-c-api.c | ||
| test-mtmd-impl.cpp | ||
| test-opt.cpp | ||
| test-peg-parser.cpp | ||
| test-quant-type-selection.cpp | ||
| test-quantize-fns.cpp | ||
| test-quantize-perf.cpp | ||
| test-quantize-stats.cpp | ||
| test-reasoning-budget.cpp | ||
| test-recurrent-state-rollback.cpp | ||
| test-rope.cpp | ||
| test-rpc-multi-server.cpp | ||
| test-rpc-multi-server.sh | ||
| test-rset-release.cpp | ||
| test-sampling.cpp | ||
| test-save-load-state.cpp | ||
| test-state-restore-fragmented.cpp | ||
| test-thread-safety.cpp | ||
| test-tokenizer-0.cpp | ||
| test-tokenizer-0.py | ||
| test-tokenizer-0.sh | ||
| test-tokenizer-1-bpe.cpp | ||
| test-tokenizer-1-spm.cpp | ||
| test-tokenizer-random.py | ||
| test-tokenizers-repo.sh | ||
| test-unicode.cpp | ||
| testing.h | ||