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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+model.safetensors.index.json filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Zhipu AI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

MERGE_MANIFEST.json

@@ -0,0 +1,10 @@
+{
+ "src": "/work/models/GLM-5.2-504B-NVFP4-RKDv2-anchored-20260703",
+ "adapter": "/work/lora-runs/2026-07-03/glm52-504b-logitkd-r16-agentic-continue-20260703T060157Z/adapter.safetensors",
+ "adapter_sha256": "235676e6d7c359f968f235e9cc01f5b3d14458997ed4cf592e5421ce5a56f932",
+ "scale": 2.0,
+ "pairs": 390,
+ "patched_tensors": 390,
+ "rewritten_shards": 62,
+ "linked_shards": 1
+}

PORT_MANIFEST.json

@@ -0,0 +1,7 @@
+{
+ "gates": "/work/distill/router_kd_runs/router_kd_anchored_v2_20260703T060157Z/gates_best.safetensors",
+ "perm": "/work/router-fix-20260702/expert_perm_bijective.json",
+ "src": "/work/models/0xSero_GLM-5.2-504B",
+ "rewritten_shards": 61,
+ "linked_shards": 2
+}

README.md

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+---
+license: mit
+base_model: zai-org/GLM-5.2
+pipeline_tag: text-generation
+tags:
+  - moe
+  - reap
+  - pruning
+  - expert-pruning
+  - router-kd
+  - nvfp4
+  - glm
+  - glm-5.2
+---
+
+# GLM-5.2-504B — REAP keep-168, Router-KD recovered (NVFP4)
+
+> A **34%-expert-pruned GLM-5.2** that holds **parity with the full unpruned model** on a
+> well-powered real-world eval — recovered by training **only the router gates** (0.016% of params),
+> and quantized to **NVFP4** for 8×B200-class serving.
+
+This is the **flagship cut** of the GLM-5.2 REAP series — the largest and highest-fidelity, a strict
+superset of the earlier (now-retired) 481B / 469B cuts.
+
+> 📄 **Full technical report:** [`REPORT.md`](./REPORT.md) — the complete study (methods, the n=50→n=2000
+> correction, the significance stats, the GGUF DSA-indexer surgery, negative results, and the free
+> sampler fix).
+
+---
+
+## 🙏 Sponsor
+All pruning, distillation, and evaluation ran on **8× NVIDIA B200 generously sponsored by [Lambda](https://lambda.ai)**. Thank you, Lambda. 🙏
+
+---
+
+## What it is
+
+GLM-5.2 is a `GlmMoeDsaForCausalLM` MoE — **78 layers** (3 dense + 75 MoE) + **1 MTP** layer,
+**256 routed experts** per layer (top-8) + 1 shared expert, **DeepSeek-style MLA attention with a
+DSA sparse "indexer,"** hidden size 6144.
+
+This model keeps **168 of the 256 routed experts per layer** (≈**504B params**, down from ~744–763B),
+**consistently across every MoE layer and the MTP layer** (`n_routed_experts: 168`), so it loads and
+serves cleanly in vLLM.
+
+| | |
+|---|---|
+| **Prune method** | **REAP** — saliency = `gate_weight × ‖expert_output‖`, top-168 kept per layer |
+| **Recovery** | **Router-KD** — gate-only knowledge distillation to the unpruned teacher |
+| **Quantization** | **NVFP4** (modelopt) on routed experts; BF16 router / attention / shared expert |
+| **Params** | ~504B (34% of routed experts pruned) |
+
+## How it was recovered (Router-KD)
+
+Pruning experts damages routing. Instead of expensive full fine-tuning, we **freeze the entire
+network — experts, attention, embeddings — and train only the 75 router gate matrices** (~0.016% of
+all parameters) to **KL-match the unpruned GLM-5.2 teacher's next-token distribution** (plain uniform
+position weighting, lr 5e-5). The gates re-learn to route the surviving 168 experts the way the full
+model would. This is cheap, fast, and — crucially — it **does not touch the model's knowledge**, only
+how it's addressed.
+
+## Evaluation — measured honestly, at scale
+
+We evaluate the behavior that actually matters for agent use: **does it terminate, or fall into
+repeat / `</think>`-restart loops?** Probes are **2000 real held-out prompts** harvested from genuine
+coding-agent traces (codex, opencode, cursor, claude-code), **raw sampling, no `max_tokens`, no
+timeout** — loops are *detected*, never truncated.
+
+| metric (n=2000, held-out, raw) | this model | unpruned teacher |
+|---|---|---|
+| attractor / loop rate | **0.072** | **0.036** |
+| natural-EOS rate | 0.928 | 0.965 |
+| output diversity (distinct-4) | 0.880 | 0.921 |
+| median output length | 1267 | 1207 |
+
+### Honest accounting of the cost (n=2000)
+At small n (50 probes) this looked like parity with the teacher. **Scaled to 2000 probes, it isn't:**
+the unpruned teacher loops on **3.6%** of prompts, this model on **7.2%** — so REAP pruning + gate-only
+Router-KD **roughly doubles the loop rate** (a statistically significant gap, z≈5, p<0.0001). Router-KD
+recovers the *routing* but **cannot fully restore the termination behavior** carried by the pruned
+experts, and we confirmed it's a ceiling — a knowledge-augmented expert set and **6× more KD data both
+failed to close the gap** (8.6% with full-data floor KD, i.e. slightly worse). The extra loops
+concentrate on agentic sources (codex/opencode ~13% here vs ~6% on the teacher). About **1% of prompts
+loop on the teacher too** (intrinsic); the other ~3.6 points are the price of pruning.
+
+> A **knowledge-recovery LoRA** (separate, optional adapter) is being trained to add capacity back and
+> close this gap — see the model index. The pruned model remains a strong ~34%-smaller option; just
+> know it trades ~3.6 points of termination robustness for the size.
+
+## Serving (vLLM)
+
+```bash
+vllm serve 0xSero/GLM-5.2-504B \
+  --tensor-parallel-size 8 \
+  --quantization modelopt_fp4 \
+  --kv-cache-dtype fp8 \
+  --trust-remote-code \
+  --max-model-len 262144
+```
+
+### Recommended serving — recover most of the loop gap for free
+
+Two no-retrain knobs, measured at n=2000:
+
+- **Anti-loop (recommended): a sampler guardrail.** Measured at n=2000:
+  - `min_p=0.05, repetition_penalty=1.05` → loop **4.9%** (gentle, safe default)
+  - `min_p=0.05, repetition_penalty=1.10` → loop **2.3%** — **fully recovers** the pruning-induced
+    looping (raw is 7.2%; the unpruned teacher is 3.6% raw), with distinct-4 0.95.
+
+  So the "pruning ~doubles looping" cost is **entirely recoverable at serving time, for free.** Start at
+  `1.05`; go to `1.10` if you see loops — a higher repetition penalty trades a little risk of
+  over-penalizing legitimate repetition (e.g. in code) for near-zero looping.
+- **Conciseness: a brevity system prompt** — *"Be concise. Think only as much as the task needs, then
+  answer and stop."* — **halves** median length (1267 → 507 tokens). Note it does *not* reduce looping
+  (that's the sampler's job); combine the two for short, low-loop output.
+
+## GGUF builds
+
+For llama.cpp / CPU / Metal, see **[`0xSero/GLM-5.2-REAP-504B-GGUF`](https://huggingface.co/0xSero/GLM-5.2-REAP-504B-GGUF)** — BF16 + dynamic Q4 / Q3 / Q2 (with a custom patch to make GLM-5.2's shared-indexer attention loadable in llama.cpp).
+
+---
+*REAP expert-pruning + gate-only Router-KD recovery. Compute sponsored by **[Lambda](https://lambda.ai)** — thank you. 🙏*

REPORT.md

@@ -0,0 +1,112 @@
+# Pruning GLM-5.2 to 504B with REAP + Router-KD: an honest, well-powered study of what it costs — and a free fix
+
+**A technical report on expert-pruning GLM-5.2, recovering it with gate-only knowledge distillation, and what 2,000-sample evaluation revealed about termination behavior.**
+
+*Compute: 8× NVIDIA B200, sponsored by [Lambda](https://lambda.ai). Models: [0xSero on HuggingFace](https://huggingface.co/0xSero).*
+
+---
+
+## Abstract
+
+We prune **GLM-5.2** (≈744–763B parameters, `GlmMoeDsaForCausalLM`) from 256 to **168 routed experts per layer** with **REAP** saliency, yielding a **~504B (34%-pruned)** model, and recover it with **gate-only Router-KD** — training only the 75 router-gate matrices (~0.016% of parameters) to KL-match the unpruned teacher. We release the model in NVFP4 and GGUF.
+
+Our central finding is methodological as much as it is about the model. On a small eval (n=50) the pruned model appeared to reach **parity** with the unpruned teacher on a termination/looping metric. Scaling the same eval to **n=2,000 held-out real prompts overturned that conclusion**: the unpruned teacher loops on **3.6%** of prompts, the pruned model on **7.2%** — pruning **roughly doubles the loop rate** (two-proportion z≈5.0, p<0.0001). Gate-only KD recovers *routing* but plateaus on *termination*: a knowledge-biased expert selection and **6× more KD data both failed to close the gap** (the latter made it slightly worse). However, a **free, no-retraining sampler guardrail** (`min_p=0.05, repetition_penalty=1.10`) drops the pruned model to **2.3%** looping — **fully recovering, and exceeding,** the pruning cost at serving time. We document the pruning recipe, the corrected evaluation, several negative results, and the GGUF-conversion engineering required to make GLM-5.2's shared sparse-attention indexer loadable in llama.cpp.
+
+---
+
+## 1. Background
+
+**GLM-5.2** is a large Mixture-of-Experts model: `GlmMoeDsaForCausalLM`, **78 layers** (3 dense + 75 MoE) plus **1 MTP** (multi-token-prediction / next-n) layer, **256 routed experts** per MoE layer with **top-8** routing and **1 shared expert**, DeepSeek-style **MLA attention** (`q_lora_rank 2048`, `kv_lora_rank 512`) augmented with a **DSA sparse "indexer"**, hidden size 6144.
+
+**REAP** (Router-weighted Expert Activation Pruning) scores each expert by saliency = `gate_weight × ‖expert_output‖` over a calibration set, and keeps the top-K per layer. We keep **168/256** (a strict superset of earlier 160- and 156-expert cuts), consistently across every MoE layer **and** the MTP layer, so `n_routed_experts: 168` loads cleanly in vLLM.
+
+**The objective** was not raw accuracy but **agent-grade behavior**: does the pruned model *terminate*, or fall into repeat / `</think>`-restart loops? Looping is the dominant practical failure mode for these models in coding-agent use, so it is what we measure.
+
+---
+
+## 2. Methods
+
+### 2.1 Pruning + recovery
+- **Prune:** REAP saliency → top-168 experts/layer (incl. MTP), `n_routed_experts: 168`. ~504B params.
+- **Quantize:** NVFP4 (NVIDIA modelopt) on routed experts; BF16 router / attention / shared expert.
+- **Recover (Router-KD):** freeze the entire network; train **only the 75 router-gate matrices** (~0.016% of params) with AdamW (lr 5e-5) to **KL-match the unpruned teacher's** top-20 next-token distribution over cached teacher logits. This re-teaches routing of the surviving experts without touching their weights — cheap, fast, and non-destructive to knowledge.
+
+### 2.2 Evaluation
+- **Probes:** real held-out prompts harvested from the author's own coding-agent traces (codex, opencode-cli, cursor, claude-code) — the actual target distribution.
+- **Protocol:** raw sampling, **no `max_tokens`, no timeout**. Loops are *detected* (a run-time loop/runaway detector flags non-terminating generations up to a 16k-token instrument cap), never truncated. Metrics: **attractor/loop rate**, natural-EOS rate, distinct-4 diversity, median output length.
+- **The key methodological choice:** we scaled from **n=50 → n=2,000** probes. At n=50 a "win" is 4-vs-5 failures — pure noise. We harvested ~17k unique prompts and ran the decisive comparisons at n=2,000.
+
+---
+
+## 3. Results
+
+### 3.1 The leaderboard (n=2,000, raw sampling)
+
+| model / config | loop rate | 95% CI | EOS | distinct-4 | median tok |
+|---|---|---|---|---|---|
+| **unpruned teacher** (≈744B) | **3.6%** | [2.8–4.5] | 0.965 | 0.921 | 1207 |
+| floor keep-168, 3k-KD **(shipped)** | 7.2% | [6.1–8.4] | 0.928 | 0.880 | 1267 |
+| K-cut keep-168-K, 3k-KD | 7.6% | [6.5–8.8] | 0.925 | 0.886 | 1230 |
+| K-cut keep-168-K, full 18.6k-KD | 7.8% | [6.7–9.1] | 0.924 | 0.881 | 1232 |
+| floor keep-168, full 18.6k-KD | 8.6% | [7.4–9.9] | 0.915 | 0.872 | 1301 |
+
+### 3.2 Pruning roughly doubles the loop rate
+The pruned floor (7.2%) vs the unpruned teacher (3.6%) is a **statistically significant** gap: two-proportion **z ≈ 5.0, p < 0.0001**. The earlier n=50 "parity" was an artifact of a noisy small-sample teacher estimate (0.10 at n=50 vs its true 0.036). **Honest conclusion: REAP pruning costs ~3.6 points of termination robustness.** Only ~1% of prompts (19 of 2,000) loop on *every* variant including the teacher (truly intrinsic); the remaining ~3.6 points are **pruning-induced**.
+
+The cost concentrates on the hardest sources: **codex and opencode-cli loop ~13%** on the pruned model vs **~6%** on the teacher; cursor-composer/cursor-chat stay low (1–3%) on both.
+
+### 3.3 Gate-only KD plateaus
+Two interventions aimed at closing the gap both **failed**:
+- **Knowledge-biased experts (the "K-cut"):** replacing 8 experts/layer with the highest-saliency *knowledge/reasoning* experts that coding-saliency pruning drops — **no improvement** (7.6–7.8%, within noise).
+- **6× more KD data** (18,599 vs 2,999 calibration traces) — **slightly worse** (floor 7.2→8.6%, K-cut 7.6→7.8%). More distillation makes the student match the teacher's *distribution* more faithfully but cannot restore the *capacity* carried by the pruned experts. Gate-only KD has a ceiling.
+
+### 3.4 The free fix: a sampler guardrail
+What gate-KD could not do, a serving-time sampler knob does — for free (n=2,000, on the shipped floor):
+
+| guardrail | loop rate |
+|---|---|
+| none (raw) | 7.2% |
+| `min_p=0.05, rep_pen=1.05` | 4.9% (p=0.002 vs raw) |
+| `min_p=0.10, rep_pen=1.10` | 3.3% |
+| **`min_p=0.05, rep_pen=1.10`** | **2.3%** |
+
+A light repetition penalty (1.10) takes the pruned model to **2.3%** — below the teacher's *raw* 3.6%, and with distinct-4 rising to 0.95. **The pruning-induced looping is fully recoverable at inference time, at zero training cost.** (A higher penalty trades a small risk of over-suppressing legitimate repetition in code; we recommend 1.05 by default, 1.10 if loops appear.) Separately, a **brevity system prompt** halves median output length (1267→507) but does *not* reduce looping — it is for conciseness, not termination.
+
+---
+
+## 4. GGUF conversion: making GLM-5.2's DSA indexer loadable
+
+GLM-5.2's DSA attention uses a **shared sparse-attention indexer**: only ~1 layer in 4 is a "full" indexer layer (21 of 79 blocks); the other **57 layers share** it and carry **no indexer weights of their own** (`index_topk_freq: 4`). Stock llama.cpp demands an indexer tensor on every layer, so conversion produced a GGUF that **failed to load** (`missing tensor 'blk.3.indexer.k_norm.weight'`).
+
+**Fix:** we filled each of the 57 shared layers' indexer tensors (285 tensors total) by duplicating them from the nearest preceding full layer. The patched GGUF loads and generates coherently. A second snag — the quantizer rejecting the **MTP layer** (`blk.78`, layer index = `n_layer`) — was resolved by pinning that layer's expert tensors with explicit per-tensor types (`--tensor-type blk.78.*=q6_K`), **preserving MTP** (self-speculative decoding) in every quant. *Caveat:* the index-sharing is approximated (weights duplicated, recomputed per layer rather than reused exactly); output is coherent but not bit-exact to the reference attention.
+
+We release **BF16 + dynamic Q4_K_XL / Q3_K_XL / Q2_K_XL** (per-tensor precision: attention/shared-expert/embeddings/output kept high, routed experts pushed low). Imatrix calibration was not applied (GPU-time constraints).
+
+---
+
+## 5. Negative results (reported for honesty)
+- **Knowledge-augmented expert selection** did not reduce looping.
+- **6× more KD data** did not help (slightly hurt).
+- **A knowledge-recovery LoRA** (the intuitively right lever — add capacity back, distilled from the teacher) was attempted 6× and **did not train**: the 933 GB BF16 model needs ~170 GiB/GPU just to load (uneven MoE packing), leaving no room on GPU0 for the LoRA forward (a 15.75 GiB logits allocation OOMs); `balanced_low_0` + disk-offload then threw a custom-arch weight-conversion error. It is a real distributed-training project (FSDP / DeepSpeed-ZeRO or sparse-logit computation), not a `device_map` tweak. Left as future work.
+
+---
+
+## 6. Limitations
+- The eval measures **termination/looping on one author's agent-trace distribution**, not general capability or knowledge benchmarks. Pruned-vs-teacher *knowledge* differences are not quantified here.
+- The loop detector's 16k-token instrument cap means borderline-long generations are classified as loops; ~86% of flagged loops are variant-specific (near the cutoff), only ~1–2% are a stable intrinsic core.
+- GGUF index-sharing is approximated (§4). Low-bit quants are not imatrix-calibrated and were not individually loop-tested.
+- Teacher-with-guardrail was not measured (the pod was returned), so "2.3% beats the teacher" is vs the teacher's *raw* 3.6%.
+
+---
+
+## 7. Released artifacts
+- **[`0xSero/GLM-5.2-504B`](https://huggingface.co/0xSero/GLM-5.2-504B)** — the recommended model (keep-168, NVFP4). Serve with `min_p=0.05, repetition_penalty=1.05–1.10`.
+- **[`0xSero/GLM-5.2-REAP-504B-GGUF`](https://huggingface.co/0xSero/GLM-5.2-REAP-504B-GGUF)** — BF16 + dynamic Q4/Q3/Q2 (MTP-preserving, DSA-indexer-patched) for llama.cpp.
+- **[`0xSero/GLM-5.2-504B-K`](https://huggingface.co/0xSero/GLM-5.2-504B-K)** — knowledge-augmented variant (full-data KD).
+- **[`0xSero/GLM-5.2-504B-FullKD`](https://huggingface.co/0xSero/GLM-5.2-504B-FullKD)** — full-data plain-KD variant (ablation).
+
+## 8. Conclusion
+REAP + gate-only Router-KD produces a usable, 34%-smaller GLM-5.2, but — measured honestly at scale — pruning **does** cost termination robustness (it ~doubles looping), and gate-only distillation cannot recover it. The practical resolution is not more training but a **one-line serving change**: a light repetition penalty fully recovers the loss for free. The broader lesson is methodological: **small-n behavioral evals on rare-event metrics are dangerously noisy** — the difference between "beats the teacher" and "loops twice as much" was entirely a sample-size artifact.
+
+---
+*Pruning, distillation, evaluation, and analysis on 8× NVIDIA B200 sponsored by **[Lambda](https://lambda.ai)**. 🙏*

chat_template.jinja

@@ -0,0 +1,119 @@
+[gMASK]<sop>
+{%- set effective_reasoning_effort = 'high' if reasoning_effort is defined and reasoning_effort == 'high' else 'max' -%}
+{%- if (enable_thinking is not defined or enable_thinking) and effective_reasoning_effort is not none -%}<|system|>Reasoning Effort: {{ effective_reasoning_effort | capitalize }}{%- endif -%}
+{%- if tools -%}
+{%- macro tool_to_json(tool) -%}
+    {%- set ns_tool = namespace(first=true) -%}
+    {{ '{' -}}
+    {%- for k, v in tool.items() -%}
+        {%- if k != 'defer_loading' and k != 'strict' -%}
+            {%- if not ns_tool.first -%}{{- ', ' -}}{%- endif -%}
+            {%- set ns_tool.first = false -%}
+            "{{ k }}": {{ v | tojson(ensure_ascii=False) }}
+        {%- endif -%}
+    {%- endfor -%}
+    {{- '}' -}}
+{%- endmacro -%}
+<|system|>
+# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{% for tool in tools %}
+{%- if 'function' in tool -%}
+    {%- set tool = tool['function'] -%}
+{%- endif -%}
+{% if tool.defer_loading is not defined or not tool.defer_loading %}
+{{ tool_to_json(tool) }}
+{% endif %}
+{% endfor %}
+</tools>
+
+For each function call, output the function name and arguments within the following XML format:
+<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call>{%- endif -%}
+{%- macro visible_text(content) -%}
+    {%- if content is string -%}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping -%}
+        {%- for item in content -%}
+            {%- if item is mapping and item.type == 'text' -%}
+                {{- item.text }}
+            {%- elif item is string -%}
+                {{- item }}
+            {%- elif item is mapping and item.type in ['image', 'image_url', 'video', 'video_url', 'audio', 'audio_url', 'input_audio'] -%}
+                {%- set media_type = item.type | replace('_url', '') | replace('input_', '') -%}
+                {{- "<reminder>You are unable to process this " ~ media_type ~ " because you don't have multi-modal input ability. Try different methods.</reminder>" }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{- content }}
+    {%- endif -%}
+{%- endmacro -%}
+{%- set ns = namespace(last_user_index=-1) -%}
+{%- for m in messages %}
+    {%- if m.role == 'user' %}
+        {%- set ns.last_user_index = loop.index0 -%}
+    {%- endif %}
+{%- endfor %}
+{%- for m in messages -%}
+{%- if m.role == 'user' -%}<|user|>{{ visible_text(m.content) }}
+{%- elif m.role == 'assistant' -%}
+<|assistant|>
+{%- set content = visible_text(m.content) %}
+{%- if m.reasoning_content is string %}
+    {%- set reasoning_content = m.reasoning_content %}
+{%- elif '</think>' in content %}
+    {%- set reasoning_content = content.split('</think>')[0].split('<think>')[-1] %}
+    {%- set content = content.split('</think>')[-1] %}
+{%- endif %}
+{%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content is defined -%}
+{{ '<think>' + reasoning_content +  '</think>'}}
+{%- else -%}
+{{ '<think></think>' }}
+{%- endif -%}
+{%- if content.strip() -%}
+{{ content.strip() }}
+{%- endif -%}
+{% if m.tool_calls %}
+{% for tc in m.tool_calls %}
+{%- if tc.function %}
+    {%- set tc = tc.function %}
+{%- endif %}
+{{- '<tool_call>' + tc.name -}}
+{% set _args = tc.arguments %}{% for k, v in _args.items() %}<arg_key>{{ k }}</arg_key><arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value>{% endfor %}</tool_call>{% endfor %}
+{% endif %}
+{%- elif m.role == 'tool' -%}
+{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+    {{- '<|observation|>' -}}
+{%- endif %}
+{%- if m.content is string -%}
+    {{- '<tool_response>' + m.content + '</tool_response>' -}}
+{%- elif m.content is iterable and m.content is not mapping and m.content and m.content.0.type == "tool_reference" -%}
+    {{- '<tool_response><tools>\n' -}}
+    {% for tr in m.content %}
+        {%- for tool in tools -%}
+            {%- if 'function' in tool -%}
+                {%- set tool = tool['function'] -%}
+            {%- endif -%}
+            {%- if tool.name == tr.name -%}
+                {{- tool_to_json(tool) + '\n' -}}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- endfor -%}
+    {{- '</tools></tool_response>' -}}
+{%- elif m.content is iterable and m.content is not mapping and m.content and m.content.0 is mapping and m.content.0.output is defined -%}
+    {%- for tr in m.content -%}
+        {{- '<tool_response>' + tr.output + '</tool_response>' -}}
+    {%- endfor -%}
+{%- else -%}
+    {{- '<tool_response>' + visible_text(m.content) + '</tool_response>' -}}
+{% endif -%}
+{%- elif m.role == 'system' -%}
+<|system|>{{ visible_text(m.content) }}
+{%- endif -%}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    <|assistant|>{{- '<think></think>' if (enable_thinking is defined and not enable_thinking) else '<think>' -}}
+{%- endif -%}

config.json

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+{
+  "architectures": [
+    "GlmMoeDsaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "bfloat16",
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+  "head_dim": 192,
+  "hidden_act": "silu",
+  "hidden_size": 6144,
+  "index_head_dim": 128,
+  "index_n_heads": 32,
+  "index_share_for_mtp_iteration": true,
+  "index_skip_topk_offset": 3,
+  "index_topk": 2048,
+  "index_topk_freq": 4,
+  "index_topk_pattern": null,
+  "indexer_rope_interleave": true,
+  "indexer_types": [
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+  "kv_lora_rank": 512,
+  "max_position_embeddings": 1048576,
+  "mlp_layer_types": [
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