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.gitattributes

@@ -33,3 +33,4 @@ 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

README.md

@@ -0,0 +1,101 @@
+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - merge
+  - model-merging
+  - crane
+  - qwen3-next
+  - moe
+  - code
+  - reasoning
+base_model:
+  - Qwen/Qwen3-Next-80B-A3B-Instruct
+  - Qwen/Qwen3-Next-80B-A3B-Thinking
+---
+
+# crane-next-80b
+
+This model was produced by **merging** two Qwen3-Next-80B-A3B checkpoints with the
+**CRANE** method — *Constrained Reasoning Injection for Code Agents via Nullspace Editing*.
+
+> 🔗 **CRANE project page:** https://rpi-nsl.github.io/CRANE/
+
+It is **not** a from-scratch or fine-tuned model. It is the result of a training-free
+weight merge that injects reasoning capability from a *thinking* donor into an
+*instruct* code-agent base, while constraining the edit to the donor's task subspace
+so that the base model's behavior is preserved.
+
+## How it was made (CRANE)
+
+CRANE treats reasoning injection as a constrained edit on top of an instruct model. Given
+an instruct base `θ_I` and the task vector `δ` (the parameter difference toward a reasoning
+donor), the merged weights are:
+
+```
+θ_M = θ_I + Π_τ( α_c · T(δ) )
+```
+
+- **`T(δ)`** — a denoising transform on the task vector (median-threshold sparsification),
+  keeping only the parameters that carry reasoning signal.
+- **`α_c = S_reason · α_base`** — per-component scaling, where `S_reason` is a Taylor /
+  gradient-based importance score estimating how much each component contributes to reasoning.
+- **`Π_τ`** — a **nullspace / subspace projection** that constrains the injected update to a
+  format-preserving subspace, so the donor's reasoning is added without overwriting the base
+  model's instruction-following and output format.
+
+This particular checkpoint uses the M1 recipe (`α_base = 0.15`) with the architecture-aware
+norm-handling variant on the Qwen3-Next-80B backbone.
+
+## Architecture
+
+This model inherits the **Qwen3-Next-80B-A3B** architecture (`model_type: qwen3_next`):
+
+| Property | Value |
+|---|---|
+| Architecture | `Qwen3NextForCausalLM` |
+| Total parameters | ~80B (≈3B active per token) |
+| Hidden size | 2048 |
+| Layers | 48 |
+| Experts | 512, top-10 (+ 1 shared expert) |
+| Attention | Hybrid — full attention every 4th layer, gated linear (DeltaNet) attention elsewhere |
+| Context length | 262,144 |
+| Vocab size | 151,936 |
+| Dtype | bfloat16 |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "zmzfpc/crane-next-80b"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="bfloat16",
+    device_map="auto",
+)
+
+messages = [{"role": "user", "content": "Write a Python function that returns the n-th Fibonacci number."}]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(inputs, max_new_tokens=512)
+print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+A recent `transformers` build with Qwen3-Next support is required (the model was exported
+with `transformers >= 4.57.0.dev0`).
+
+## Intended use & limitations
+
+This is a **research artifact** demonstrating reasoning injection via model merging on a
+large MoE model. It targets code-agent / reasoning workloads. As a merged model it may
+exhibit behaviors of either parent and has not been separately safety-tuned; evaluate before
+any production use.
+
+## Citation / attribution
+
+Method: **CRANE — Constrained Reasoning Injection for Code Agents via Nullspace Editing**,
+https://rpi-nsl.github.io/CRANE/
+
+Base models: Qwen3-Next-80B-A3B (Instruct & Thinking), © the Qwen team, Apache-2.0.

config.json

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+{
+  "architectures": [
+    "Qwen3NextForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "decoder_sparse_step": 1,
+  "eos_token_id": 151645,
+  "full_attention_interval": 4,
+  "head_dim": 256,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 5120,
+  "linear_conv_kernel_dim": 4,
+  "linear_key_head_dim": 128,
+  "linear_num_key_heads": 16,
+  "linear_num_value_heads": 32,
+  "linear_value_head_dim": 128,
+  "max_position_embeddings": 262144,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_next",
+  "moe_intermediate_size": 512,
+  "norm_topk_prob": true,
+  "num_attention_heads": 16,
+  "num_experts": 512,
+  "num_experts_per_tok": 10,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 2,
+  "output_router_logits": false,
+  "partial_rotary_factor": 0.25,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000000,
+  "router_aux_loss_coef": 0.001,
+  "shared_expert_intermediate_size": 512,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.57.0.dev0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.57.0.dev0"
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+            "special": false
+        },
+        "151665": {
+            "content": "<tool_response>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151666": {
+            "content": "</tool_response>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151667": {
+            "content": "<think>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        },
+        "151668": {
+            "content": "</think>",
+            "lstrip": false,
+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": false
+        }
+    },
+    "additional_special_tokens": [
+        "<|im_start|>",
+        "<|im_end|>",
+        "<|object_ref_start|>",
+        "<|object_ref_end|>",
+        "<|box_start|>",
+        "<|box_end|>",
+        "<|quad_start|>",
+        "<|quad_end|>",
+        "<|vision_start|>",
+        "<|vision_end|>",
+        "<|vision_pad|>",
+        "<|image_pad|>",
+        "<|video_pad|>"
+    ],
+    "bos_token": null,
+    "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}",
+    "clean_up_tokenization_spaces": false,
+    "eos_token": "<|im_end|>",
+    "errors": "replace",
+    "model_max_length": 1010000,
+    "pad_token": "<|endoftext|>",
+    "split_special_tokens": false,
+    "tokenizer_class": "Qwen2Tokenizer",
+    "unk_token": null,
+    "add_bos_token": false
+}