Add NexaMat controller Qwen3-14B prod candidate checkpoint

.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
+multimodal/controller/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/tokenizer.json filter=lfs diff=lfs merge=lfs -text

multimodal/controller/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/README.md

@@ -0,0 +1,29 @@
+---
+license: other
+tags:
+- nexamat
+- materials-science
+- qwen3
+- controller
+- multimodal
+---
+
+# NexaMat Controller Candidate
+
+Run: `nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z`
+
+This is a promoted controller candidate for NexaMat stack-alignment experiments.
+It is a full fine-tuned Qwen3-14B checkpoint exported from FSDP shards into a reloadable Hugging Face model directory.
+
+## Alignment Eval
+
+- Alignment score: `1.0`
+- Gates passed: `10/10`
+- Structured output validity: `1.0`
+- Candidate decision accuracy: `1.0`
+- Controller promotable: `True`
+
+## Notes
+
+This artifact should be treated as a controller candidate, not the final full NexaMat stack.
+Routine experiment checkpoints should not be uploaded at this size; upload cadence should be limited to promoted milestones.

multimodal/controller/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/added_tokens.json

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+  "</tool_call>": 151658,
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multimodal/controller/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# 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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

multimodal/controller/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/checkpoint_receipt.json

@@ -0,0 +1,10 @@
+{
+  "has_config": true,
+  "has_tokenizer": true,
+  "model_dir": "/dev/shm/nexa_mat_controller_runs/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/final_model_merged",
+  "reloadable_hf_model": true,
+  "run_id": "nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z",
+  "weight_files": [
+    "model.safetensors"
+  ]
+}

multimodal/controller/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/config.json

@@ -0,0 +1,72 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 17408,
+  "layer_types": [
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 40,
+  "model_type": "qwen3",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

multimodal/controller/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/controller_alignment_eval.md

@@ -0,0 +1,29 @@
+# NexaMat Controller Output Alignment Eval
+
+## Scope
+
+This evaluates model-produced controller JSON against stack-native target traces. It is stricter than language loss and more relevant than one-shot generation: the controller must route candidates according to physics validity, novelty, knownness, evidence, and hallucination containment.
+
+Source: `/dev/shm/nexa_mat_controller_runs/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/controller_alignment_model_eval/predictions.jsonl`
+
+## Result
+
+- Records: `1`
+- Candidates: `6`
+- Structured output validity: `100.00%`
+- Candidate decision accuracy: `100.00%`
+- Useful candidate yield at K: `100.00%`
+- Frontier DFT preservation rate: `100.00%`
+- Known-material deprioritization rate: `100.00%`
+- False-positive rate on bad candidates: `0.00%`
+- Uncontained hallucination rate: `0.00%`
+
+## Alignment
+
+- Alignment score: `1.000`
+- Gates passed: `10/10`
+- Ready for FFT: `True`
+
+## Interpretation
+
+Passing this eval means the semantic controller can follow the stack contract on labeled traces. It does not prove the final model is scientifically accurate, but it does prove the control policy is measurable: invalid physics is not promoted, frontier low-evidence candidates can be routed to DFT, known candidates can be deprioritized, and unsupported evidence claims are contained.

multimodal/controller/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/controller_alignment_summary.json

@@ -0,0 +1,32 @@
+{
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+  "alignment_score": 1.0,
+  "candidate_decision_accuracy": 1.0,
+  "candidates": 6,
+  "controller_alignment_promotable": true,
+  "criteria": {
+    "alignment_ready_for_fft": true,
+    "bad_candidate_false_positive_max": 0.0,
+    "min_frontier_preservation": 1.0,
+    "min_known_deprioritization": 1.0,
+    "uncontained_hallucination_max": 0.0
+  },
+  "false_positive_rate_on_bad_candidates": 0.0,
+  "frontier_preservation_rate": 1.0,
+  "gate_checks": {
+    "bad_candidate_false_positive": true,
+    "frontier_preservation": true,
+    "known_deprioritization": true,
+    "ready_for_fft": true,
+    "uncontained_hallucination": true
+  },
+  "known_deprioritization_rate": 1.0,
+  "prediction_generators": [
+    "/dev/shm/nexa_mat_controller_runs/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/final_model_merged"
+  ],
+  "ready_for_fft": true,
+  "records": 1,
+  "report": "/dev/shm/nexa_mat_controller_runs/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/controller_alignment_model_eval_patched/controller_alignment_eval.md",
+  "structured_output_validity": 1.0,
+  "uncontained_hallucination_rate": 0.0
+}

multimodal/controller/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/generation_config.json

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+  "do_sample": true,
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+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.6",
+  "trust_remote_code": true
+}

multimodal/controller/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/model.safetensors

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multimodal/controller/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/special_tokens_map.json

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+}

multimodal/controller/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/tokenizer.json

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+size 11422753

multimodal/controller/nexa_mat_controller_qwen3_14b_stack_prod_candidate_20260518T221530Z/tokenizer_config.json

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+}