diff --git a/pass3-hope/LICENSE b/pass3-hope/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..986b06fb2916b3518b82c7dcc0870723b91f2f92 --- /dev/null +++ b/pass3-hope/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2026 Z.AI Co., Ltd + +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. diff --git a/pass3-hope/README.md b/pass3-hope/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c22cd4eb458e524c0d051b89818050c27fc3fd04 --- /dev/null +++ b/pass3-hope/README.md @@ -0,0 +1,136 @@ +--- +license: mit +base_model: zai-org/GLM-5.3-Flash +tags: [reap, moe, pruning, glm5_next, jetson, thor] +--- + +# GLM-5.3-Flash REAP-50 (FP8) + +50% of routed experts removed with **REAP** +(Router-weighted Expert Activation Pruning, arXiv:2510.13999), calibrated on a +permissively-licensed multi-domain corpus that includes real image-text pairs. + +| | | +|---|---| +| Base | `zai-org/GLM-5.3-Flash` (MIT, FP8 E4M3, 128x128 block scales) | +| Experts | 288 -> 144 per layer, top-8 routing unchanged | +| Size | 160.6 GiB (FP8) | +| Healed | yes | +| MTP block | excluded (see below) | + +## Why FP8 and not BF16 + +The upstream release is **FP8**, not BF16. Routed experts are stored per-expert with their own +`weight_scale_inv` block scales, so pruning is deleting whole tensors - **lossless on every +retained weight**. The 642 GB BF16 repo elsewhere on the Hub is a dequantised upcast carrying +no additional information. + +## Calibration + +Mixture weighted for a coding/agentic model that stays empirically grounded: agentic 24%, +code 21%, math 15%, multimodal 15%, science+bio 10%, finance 8%, ballast 7%. +Permissive licences only, so this checkpoint keeps the base model's MIT lineage. + +Vision is first-class: the vision tower contains no MoE and is untouched, but image tokens +route through the same expert pool as text, so text-only calibration would have deleted +vision-serving experts with certainty. Real image-text pairs were asserted present. + +## Evaluation status: NONE + +**This checkpoint has not been evaluated.** No benchmark has been run against it - not coding, +not agentic, not vision, not knowledge. What has been verified is *structural*: expert counts +match the config, routers are sliced to the retained set, every tensor loads, the vision tower +is untouched. The MTP block at layer 45 is cleanly absent. + +The pruning itself measured **1.34x better than random** at retaining expert output +contribution (saliency mass 0.669 against 0.50 for random pruning at the same ratio). That says +the criterion selected well. It does **not** say the model is good. + +Treat this as a research artifact pending evaluation, not a drop-in replacement. + +## What this prune costs, and what it does not + +REAP ranks experts on saliency pooled across the calibration mixture, so the cost is **not** +spread evenly across capabilities. Measured on this checkpoint - the fraction of the expert +output each domain actually relies on that survived the prune: + +| domain | retained | +|---|---| +| agentic / tool use | 0.747 | +| code | 0.728 | +| science | 0.720 | +| maths | 0.713 | +| vision (image-text) | 0.682 | +| finance | 0.651 | +| general / ballast | **0.487** | + +**The damage is concentrated where retrieval can repair it.** Generic factual ballast is the +worst-retained bucket, and it is also the single most RAG-recoverable capability: a markdown +corpus and a retriever substitute for memorised trivia almost perfectly. The best-retained +buckets - agentic behaviour, code, science, maths - are the ones retrieval **cannot** restore, +because you cannot retrieve your way to reasoning, tool use, or code synthesis. + +If you are deploying this, pair it with retrieval. That is not a workaround for a defect; it is +the shape the trade was made in. A uniformly-quantised model of the same footprint spends its +degradation budget evenly, including on the capabilities retrieval cannot give back. + +Two honest caveats. Low retention can also mean a domain uses experts *diffusely* rather than +that its capability was removed - generic web text spreads across many experts while REAP keeps +concentrated ones. And the per-domain spread was a **consequence** of the calibration mixture, +not a targeted design: this is mixture-shaped preservation, not surgery. + +## Method: strengths and gaps + +**Where this is above common practice** + +- Calibration is 0.0M tokens across seven use-case-weighted domains, against the 128-512 generic + samples typical of pruning and quantisation work. +- **Real image-text pairs** in calibration, not text descriptions of images. Measured effect: + vision retained 0.682, at the cross-domain mean. Text-only calibration deletes + vision-serving experts with near-certainty. +- Saliency capture was **verified against the model source** - the router gate excludes + `e_score_correction_bias`, which is the detail most implementations get silently wrong. +- Per-token router scores were cached, making post-prune routing exactly replayable offline. +- The healing correction was **measured, not derived**. The standard first-moment estimator + over-corrects by ~30% on this architecture because it ignores `norm_topk_prob` + renormalisation; that error was found and fixed only because of the router cache. +- The calibration budget was validated by a split-half stopping rule on retained saliency mass, + rather than assumed. + +**Where it falls short, stated plainly** + +- **Healing rescales experts; it does not retrain them.** The per-expert coefficients are the best + a *fixed rescaling* can do, and that ceiling is now measured rather than assumed: 96.6% of an + expert's output energy is token-dependent residual rather than its mean, so the ~0.27 residual + that remains is information deleted with the pruned experts, not error a better fit could + remove. Recovering it means layer-local distillation, which needs a backward pass that does not + fit the hardware this was built on. +- **No expert merging** (REAM/EEP), rejected on cost. +- **Evaluation is teacher-forced dNLL and top-1 agreement, not generative benchmarks.** That is + the largest gap: dNLL does not fully predict agentic or coding capability, which is what this + model is for. Generation from the unpruned teacher costs ~110 s/token on one device and was + out of reach. +- **Long context is uncalibrated.** Calibration sequences were capped at 2048 tokens against a + 1M-token context window. +- The calibration mixture was specified in *samples* but acts in *tokens*; document lengths + differ by more than 10x, so realised domain weights differed substantially from intended. +- A single prune ratio was materialised. No 40/50/60 ablation with evaluation behind it. + +## Known limitations + +- **The MTP (multi-token-prediction) block at layer 45 is excluded.** `transformers`' `Glm5NextForConditionalGeneration` does not instantiate it, so the pruning path cannot see it. Dropping it forecloses speculative decoding from this artifact. +- REAP has no published data above 50% compression; this checkpoint sits at the validated + ceiling, not beyond it. +- Expect **factual-recall** regression before reasoning or coding regression. That is the + measured failure mode of expert pruning on this architecture family: the closest published + analogue (`cerebras/Kimi-Linear-REAP-35B-A3B`, same KDA + full-attention stack) loses 3.4 + points on FRAMES at only 30% pruning while code and maths hold flat. +- Healing is an output-scale correction applied exactly to the F32 block scales, median gain **0.9090**. It was **measured**, not derived: post-prune routing is replayed from a cached router-score trace, because the first-moment estimator that pass 1 used ignores that `norm_topk_prob` renormalises the surviving top-8 and over-corrects by ~30%. It is *not* distillation and does not recover lost knowledge. +- Routing is disrupted more than expert count suggests: the retained experts carry ~0.95x the + routing mass an average expert would, because REAP preserves rare-but-strong experts over + common-but-weak ones. + +## Serving on Jetson Thor + +Use the **cutlass** fused-MoE backend (the Marlin FP4 MoE kernel faults at >=256 experts) and +`TRITON_MLA` for the 11 MLA+DSA layers (FLASHINFER is invalid for MLA). diff --git a/pass3-hope/adapters/first_moment_gains.json b/pass3-hope/adapters/first_moment_gains.json new file mode 100644 index 0000000000000000000000000000000000000000..88681d3c0fd194bdec3c84f69f983cc6ad16f635 --- /dev/null +++ b/pass3-hope/adapters/first_moment_gains.json @@ -0,0 +1,61 @@ +{ + "method": "first-moment MoE output-scale correction (no teacher, no forward pass)", + "gains": { + "model.language_model.layers.3.mlp": 0.6929448705738448, + "model.language_model.layers.4.mlp": 0.8275264171335417, + "model.language_model.layers.5.mlp": 0.7691773829390459, + "model.language_model.layers.6.mlp": 0.7979076218052154, + "model.language_model.layers.7.mlp": 0.8078911632468451, + "model.language_model.layers.8.mlp": 0.7168322158566274, + "model.language_model.layers.9.mlp": 0.7432604169992395, + "model.language_model.layers.10.mlp": 0.7206777935447581, + "model.language_model.layers.11.mlp": 0.7626100890374157, + "model.language_model.layers.12.mlp": 0.8074106247020925, + "model.language_model.layers.13.mlp": 0.7664205662606375, + "model.language_model.layers.14.mlp": 0.7833696012262404, + "model.language_model.layers.15.mlp": 0.7185073755181517, + "model.language_model.layers.16.mlp": 0.7727838850096236, + "model.language_model.layers.17.mlp": 0.7405453344473255, + "model.language_model.layers.18.mlp": 0.7578363968274796, + "model.language_model.layers.19.mlp": 0.7945049814830348, + "model.language_model.layers.20.mlp": 0.8107061170393675, + "model.language_model.layers.21.mlp": 0.8315814016946674, + "model.language_model.layers.22.mlp": 0.8532838478790715, + "model.language_model.layers.23.mlp": 0.7831526144395292, + "model.language_model.layers.24.mlp": 0.8134628560152266, + "model.language_model.layers.25.mlp": 0.7523038883232029, + "model.language_model.layers.26.mlp": 0.7902068124487682, + "model.language_model.layers.27.mlp": 0.7184067092556089, + "model.language_model.layers.28.mlp": 0.7484535209771637, + "model.language_model.layers.29.mlp": 0.7394112083006681, + "model.language_model.layers.30.mlp": 0.7531677997209874, + "model.language_model.layers.31.mlp": 0.7816384669181902, + "model.language_model.layers.32.mlp": 0.7665805247621434, + "model.language_model.layers.33.mlp": 0.733946518892489, + "model.language_model.layers.34.mlp": 0.7216211687255433, + "model.language_model.layers.35.mlp": 0.7269947802834427, + "model.language_model.layers.36.mlp": 0.7552838646710964, + "model.language_model.layers.37.mlp": 0.8013125057378521, + "model.language_model.layers.38.mlp": 0.7670837992974577, + "model.language_model.layers.39.mlp": 0.7986716776406351, + "model.language_model.layers.40.mlp": 0.7107730855763762, + "model.language_model.layers.41.mlp": 0.7380008004885258, + "model.language_model.layers.42.mlp": 0.7772684749907915, + "model.language_model.layers.43.mlp": 0.752188080793181, + "model.language_model.layers.44.mlp": 0.6673158203927475 + }, + "stats": { + "layers": 42, + "skipped": 0, + "gain_min": 0.6673, + "gain_max": 0.8533, + "gain_median": 0.7664, + "gain_mean": 0.7636 + }, + "per_expert_layers": [], + "per_expert_tensors": 0, + "per_expert_coefficients": {}, + "experts_scaled": 6048, + "shards_rewritten": 59, + "not_done": "layer-local distillation and LoRA SFT - not tractable for 165B on a 117 GiB box; see wiki/70-healing.md" +} \ No newline at end of file diff --git a/pass3-hope/chat_template.jinja b/pass3-hope/chat_template.jinja new file mode 100644 index 0000000000000000000000000000000000000000..06bd89e94b96652fd5d76d9f9e11cfa15e09f49d --- /dev/null +++ b/pass3-hope/chat_template.jinja @@ -0,0 +1,261 @@ +[gMASK] +{%- set effective_reasoning_effort = reasoning_effort if reasoning_effort is defined and reasoning_effort in ['low', 'high'] else 'max' -%} +{%- if effective_reasoning_effort is not none -%}<|system|>Reasoning Effort: {{ effective_reasoning_effort | capitalize }}{%- endif -%} +{%- set clear_thinking = clear_thinking if clear_thinking is defined else false -%} +{%- 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 -%} +{%- macro tool_references_to_response(refs) -%} + {{- '\n' -}} + {%- for tr in refs -%} + {%- 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 -%} + {{- '' -}} +{%- endmacro -%} +<|system|> +# Tools + +You may call one or more functions to assist with the user query. + +You are provided with function signatures within XML tags: + +{% 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 %} + + +For each function call, output the function name and arguments within the following XML format: +{function-name}{arg-key-1}{arg-value-1}{arg-key-2}{arg-value-2}...{%- endif -%} +{%- macro emit_image() -%}<|begin_of_image|><|image|><|end_of_image|>{%- endmacro -%} +{%- macro emit_video() -%}<|begin_of_video|><|video|><|end_of_video|>{%- endmacro -%} +{%- macro emit_audio() -%}<|begin_of_audio|><|end_of_audio|>{%- endmacro -%} +{%- 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'] -%} + {{- emit_image() -}} + {%- elif item is mapping and item.type in ['video', 'video_url'] -%} + {{- emit_video() -}} + {%- elif item is mapping and item.type in ['audio', 'audio_url', 'input_audio'] -%} + {{- emit_audio() -}} + {%- endif -%} + {%- endfor -%} + {%- elif content is not none -%} + {{- content -}} + {%- endif -%} +{%- endmacro -%} +{%- macro tool_response(text) -%} +{{- '' + text + '' -}} +{%- endmacro -%} +{%- macro render_tool_response(m) -%} +{%- if m.content is string -%} + {{- tool_response(m.content) -}} +{%- elif m.content and m.content is not mapping and m.content.0.type == "tool_reference" -%} + {{- tool_references_to_response(m.content) -}} +{%- elif is_list_of_outputs(m) -%} + {%- for tr in m.content -%} + {%- if tr.output is iterable and tr.output is not string and tr.output is not mapping and tr.output and tr.output.0.type == "tool_reference" -%} + {{- tool_references_to_response(tr.output) -}} + {%- else -%} + {{- tool_response(visible_text(tr.output)) -}} + {%- endif -%} + {%- endfor -%} +{%- else -%} + {{- tool_response(visible_text(m.content)) -}} +{%- endif -%} +{%- endmacro -%} +{%- macro id_of(obj) -%} + {%- if obj.tool_call_id -%} + {{- obj.tool_call_id -}} + {%- elif obj.id -%} + {{- obj.id -}} + {%- endif -%} +{%- endmacro -%} +{%- macro is_list_of_outputs(m) -%} + {%- if m.content and m.content.0.output is defined -%}1{%- endif -%} +{%- endmacro -%} +{%- macro has_dup_tool_result_id(lo, hi, target) -%} + {%- set ns_cnt = namespace(n=0) -%} + {%- for k in range(lo, hi + 1) -%} + {%- set m = messages[k] -%} + {%- if is_list_of_outputs(m) -%} + {%- for entry in m.content -%} + {%- if id_of(entry) == target -%} + {%- set ns_cnt.n = ns_cnt.n + 1 -%} + {%- endif -%} + {%- endfor -%} + {%- elif id_of(m) == target -%} + {%- set ns_cnt.n = ns_cnt.n + 1 -%} + {%- endif -%} + {%- if ns_cnt.n > 1 -%}{%- break -%}{%- endif -%} + {%- endfor -%} + {%- if ns_cnt.n > 1 -%}1{%- endif -%} +{%- endmacro -%} +{%- macro tc_id_exists(tcs, target) -%} + {%- set ns_f = namespace(found=false) -%} + {%- for tc in tcs -%} + {%- if id_of(tc) == target -%} + {%- set ns_f.found = true -%} + {%- break -%} + {%- endif -%} + {%- endfor -%} + {%- if ns_f.found -%}1{%- 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 '' in content %} + {%- set reasoning_content = content.split('')[0].split('')[-1] %} + {%- set content = content.split('')[-1] %} +{%- endif %} +{%- if (not clear_thinking or loop.index0 > ns.last_user_index) and reasoning_content is defined -%} +{{ '' + reasoning_content + ''}} +{%- else -%} +{{ '' }} +{%- 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 %} +{{- '' ~ tc.name -}} +{% set _args = tc.arguments %}{% for k, v in _args.items() %}{{ k }}{{ v | tojson(ensure_ascii=False) if v is not string else v }}{% endfor %}{% endfor %} +{% endif %} +{%- elif m.role == 'tool' -%} +{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|observation|>' -}} + {%- set block_start = loop.index0 -%} + {%- set ns_blk = namespace(end=block_start) -%} + {%- for j in range(block_start, messages|length) -%} + {%- if messages[j].role == 'tool' -%} + {%- set ns_blk.end = j -%} + {%- else -%} + {%- break -%} + {%- endif -%} + {%- endfor -%} + {%- set ns_a = namespace(tool_calls=none) -%} + {%- if block_start > 0 and messages[block_start - 1].role == 'assistant' and messages[block_start - 1].tool_calls -%} + {%- set ns_a.tool_calls = messages[block_start - 1].tool_calls -%} + {%- endif -%} + {%- set ns_chk = namespace(can_sort=true) -%} + {%- if not ns_a.tool_calls -%} + {%- set ns_chk.can_sort = false -%} + {%- else -%} + {%- for k in range(block_start, ns_blk.end + 1) -%} + {%- if not ns_chk.can_sort -%}{%- break -%}{%- endif -%} + {%- set m = messages[k] -%} + {%- if is_list_of_outputs(m) -%} + {%- for entry in m.content -%} + {%- if not ns_chk.can_sort -%}{%- break -%}{%- endif -%} + {%- set eid = id_of(entry) -%} + {%- if not eid -%} + {%- set ns_chk.can_sort = false -%} + {%- elif has_dup_tool_result_id(block_start, ns_blk.end, eid) -%} + {%- set ns_chk.can_sort = false -%} + {%- elif not tc_id_exists(ns_a.tool_calls, eid) -%} + {%- set ns_chk.can_sort = false -%} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {%- set tk_id = id_of(m) -%} + {%- if not tk_id -%} + {%- set ns_chk.can_sort = false -%} + {%- elif has_dup_tool_result_id(block_start, ns_blk.end, tk_id) -%} + {%- set ns_chk.can_sort = false -%} + {%- elif not tc_id_exists(ns_a.tool_calls, tk_id) -%} + {%- set ns_chk.can_sort = false -%} + {%- endif -%} + {%- endif -%} + {%- endfor -%} + {%- for i in range(ns_a.tool_calls | length) -%} + {%- if not ns_chk.can_sort -%}{%- break -%}{%- endif -%} + {%- set tc_id = id_of(ns_a.tool_calls[i]) -%} + {%- if not tc_id -%} + {%- set ns_chk.can_sort = false -%} + {%- endif -%} + {%- for j in range(i + 1, ns_a.tool_calls | length) -%} + {%- if id_of(ns_a.tool_calls[j]) == tc_id -%} + {%- set ns_chk.can_sort = false -%} + {%- break -%} + {%- endif -%} + {%- endfor -%} + {%- endfor -%} + {%- endif -%} + {%- if ns_chk.can_sort -%} + {%- for tc in ns_a.tool_calls -%} + {%- set tc_id = id_of(tc) -%} + {%- for k in range(block_start, ns_blk.end + 1) -%} + {%- set m = messages[k] -%} + {%- if is_list_of_outputs(m) -%} + {%- for entry in m.content -%} + {%- set eid = id_of(entry) -%} + {%- if eid == tc_id -%} + {%- if entry.output is iterable and entry.output is not string and entry.output is not mapping and entry.output and entry.output.0.type == "tool_reference" -%} + {{- tool_references_to_response(entry.output) -}} + {%- else -%} + {{- tool_response(visible_text(entry.output)) -}} + {%- endif -%} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {%- set tk_id = id_of(m) -%} + {%- if tk_id == tc_id -%} + {{- render_tool_response(m) -}} + {%- endif -%} + {%- endif -%} + {%- endfor -%} + {%- endfor -%} + {%- else -%} + {%- for k in range(block_start, ns_blk.end + 1) -%} + {{- render_tool_response(messages[k]) -}} + {%- endfor -%} + {%- endif -%} +{% endif -%} +{%- elif m.role == 'system' -%} +<|system|>{{ visible_text(m.content) }} +{%- endif -%} +{%- endfor -%} +{%- if add_generation_prompt -%} + <|assistant|>{{- '' -}} +{%- endif -%} diff --git a/pass3-hope/config.json b/pass3-hope/config.json new file mode 100644 index 0000000000000000000000000000000000000000..624541b0c0a91532a4390982a62d9c855fc32184 --- /dev/null +++ b/pass3-hope/config.json @@ -0,0 +1,1818 @@ +{ + "architectures": [ + "Glm5NextForConditionalGeneration" + ], + "text_config": { + "attention_bias": false, + "attention_dropout": 0.0, + "dtype": "bfloat16", + "eos_token_id": [ + 154820, + 154827, + 154829 + ], + "first_k_dense_replace": 3, + "hc_eps": 1e-06, + "hc_mult": 4, + "hc_sinkhorn_iters": 20, + "head_dim": 0, + "hidden_act": "silu", + "hidden_size": 4096, + "index_head_dim": 128, + "index_kpool": 4, + "index_kpool_always_select_tail": true, + "index_kpool_compress": true, + "index_n_heads": 32, + "index_topk": 2048, + "index_share_for_mtp_iteration": true, + "indexer_rope_interleave": true, + "indexer_types": [ + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full", + "full" + ], + "initializer_range": 0.02, + "intermediate_size": 12288, + "kv_lora_rank": 512, + "layer_types": [ + "linear_attention", + "linear_attention", + "linear_attention", + "deepseek_sparse_attention", + "linear_attention", + "linear_attention", + "linear_attention", + "deepseek_sparse_attention", + "linear_attention", + "linear_attention", + "linear_attention", + 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