model : add support for HrmTextForCausalLM (DFM Mimir 1B) (#27625)

* 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>

tests/test-llama-archs.cpp

@@ -130,6 +130,8 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
     } else if (arch == LLM_ARCH_QWEN3TTS) {
         //n_vocab = 4096; // must be >= the hard-coded codec head size (3072)
         n_vocab = 3072; // TODO: should be 4096, but user code cannot get `n_vocab_out` yet [TAG_LLAMA_N_VOCAB_OUT]
+    } else if (arch == LLM_ARCH_HRM_TEXT) {
+        n_layer = 8; // 1 layer per stack x 2 h-cycles x (3 l-cycles + 1) cache slots
     }
 
     uint32_t n_head_kv = n_head;
@@ -325,6 +327,13 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
         ms.add_kv(LLM_KV_EXPERT_WEIGHTS_NORM,                   true);
     }
 
+    if (arch == LLM_ARCH_HRM_TEXT) {
+        // 8 cache slots alias 2 physical blocks: 1 low-stack layer + 1 high-stack layer
+        ms.add_kv(LLM_KV_HRM_LAYERS_PER_STACK, uint32_t(1));
+        ms.add_kv(LLM_KV_HRM_H_CYCLES,         uint32_t(2));
+        ms.add_kv(LLM_KV_HRM_L_CYCLES,         uint32_t(3));
+    }
+
     if (arch == LLM_ARCH_MAPLE) {
         ms.add_kv(LLM_KV_SWIGLU_CLAMP_EXP, 7.0f);
     }