Publish mn-context-engine-model-v3 with benchmark report

.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 +1,74 @@
+---
+library_name: transformers
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM3-3B
+tags:
+- smollm3
+- merged-model
+- context-compression
+- agent-memory
+- membrane
+pipeline_tag: text-generation
+---
+
+# mn-context-engine-model-v3
+
+`mn-context-engine-model-v3` is the production merged context-compression model for Membrane / MirrorNeuron. It was produced by merging the v3 DPO adapter into `HuggingFaceTB/SmolLM3-3B`, so it can be loaded directly without a separate PEFT adapter.
+
+Author: Homer Quan
+
+Related runtime: https://github.com/MirrorNeuronLab/MirrorNeuron
+
+Website: https://www.mirrorneuron.io
+
+## Intended Use
+
+Use this model as a generative context compressor for multi-agent working memory. It is optimized for preserving executable agent state under a token budget: current task, hard constraints, latest user instructions, source references, file paths, IDs, tool errors, recovery checkpoints, decisions, and next actions.
+
+For production Membrane deployments, use the hybrid runtime path when exact protected-fact preservation is contractual: model compression followed by deterministic cleanup, restoration, privacy redaction, and graph repair.
+
+## Benchmark Summary
+
+Evaluation used Membrane's 100-case mock context-compression suite. Mean ratio is `compressed_tokens / original_tokens`, so lower is more compressed. The v2 rows are included as the previous SmolLM3 LoRA reference point.
+
+| Method | Quality | Fact Recall | Hard Constraints | Pinned | Source Refs | Ratio | Private Leaks | Total Time |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|
+| SmolLM3 v2 LoRA llm_only | 0.882 | 0.942 | 1.000 | 0.750 | 0.698 | 0.496 | 0 | 3053.4s |
+| SmolLM3 v2 LoRA hybrid | 0.985 | 1.000 | 1.000 | 0.996 | 1.000 | 0.700 | 0 | 1.7s |
+| SmolLM3 v3 DPO llm_only | 0.864 | 0.916 | 1.000 | 0.713 | 0.627 | 0.518 | 0 | 1693.3s |
+| SmolLM3 v3 DPO hybrid | 0.990 | 1.000 | 1.000 | 0.998 | 1.000 | 0.751 | 0 | 1.3s |
+
+### Interpretation
+
+- `llm_only` measures the merged model as a standalone context compressor.
+- `hybrid` applies deterministic graph/fact/pin/source-ref repair after generation and is the recommended runtime contract for Membrane.
+- Exact source-reference and pinned-term retention should remain backed by deterministic validation, not only model behavior.
+
+Full benchmark reports are included under `benchmark/`.
+
+## Loading
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "homerquan/mn-context-engine-model-v3"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+    torch_dtype="auto",
+    device_map="auto",
+)
+```
+
+## Prompt Shape
+
+The model was trained for structured compression targets with compact operational sections such as `task`, `constraints`, `latest`, `decisions`, `evidence`, `errors`, `next`, `refs`, and `warnings`. Keep prompts focused on the working-memory packet to compress.
+
+## Limitations
+
+- This is a merged model, not a LoRA adapter.
+- It was evaluated on Membrane's deterministic mock-context suite; external workloads should be re-benchmarked.
+- Standalone model output can miss exact pinned terms or source refs. Use deterministic repair gates when those are contractual.
+- Do not rely on model-only compression for private-memory exclusion; keep redaction gates in the runtime path.

chat_template.jinja

@@ -0,0 +1,94 @@
+{# ───── defaults ───── #}
+{%- if enable_thinking is not defined -%}
+{%- set enable_thinking = true -%}
+{%- endif -%}
+
+{# ───── reasoning mode ───── #}
+{%- if enable_thinking -%}
+  {%- set reasoning_mode = "/think" -%}
+{%- else -%}
+  {%- set reasoning_mode = "/no_think" -%}
+{%- endif -%}
+
+{# ───── header (system message) ───── #}
+{{- "<|im_start|>system\n" -}}
+
+{%- if messages[0].role == "system" -%}
+  {%- set system_message = messages[0].content -%}
+  {%- if "/no_think" in system_message -%}
+    {%- set reasoning_mode = "/no_think" -%}
+  {%- elif "/think" in system_message -%}
+    {%- set reasoning_mode = "/think" -%}
+  {%- endif -%}
+  {%- set custom_instructions = system_message.replace("/no_think", "").replace("/think", "").rstrip() -%}
+{%- endif -%}
+
+{%- if "/system_override" in system_message -%}
+  {{- custom_instructions.replace("/system_override", "").rstrip() -}}
+  {{- "<|im_end|>\n" -}}
+{%- else -%}
+  {{- "## Metadata\n\n" -}}
+  {{- "Knowledge Cutoff Date: June 2025\n" -}}
+  {%- set today = strftime_now("%d %B %Y") -%}
+  {{- "Today Date: " ~ today ~ "\n" -}}
+  {{- "Reasoning Mode: " + reasoning_mode + "\n\n" -}}
+  
+  {{- "## Custom Instructions\n\n" -}}
+  {%- if custom_instructions -%}
+    {{- custom_instructions + "\n\n" -}}
+  {%- elif reasoning_mode == "/think" -%}
+    {{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracking, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> Thought section </think> Solution section. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion.\n\n" -}}
+  {%- else -%}
+    {{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face.\n\n" -}}
+  {%- endif -%}
+
+  {%- if xml_tools or python_tools or tools -%}
+    {{- "### Tools\n\n" -}}
+    {%- if xml_tools or tools -%}
+      {%- if tools -%}
+        {%- set xml_tools = tools -%}
+      {%- endif -%}
+      {%- set ns = namespace(xml_tool_string="You may call one or more functions to assist with the user query.\nYou are provided with function signatures within <tools></tools> XML tags:\n\n<tools>\n") -%}
+      {%- for tool in xml_tools[:] -%} {# The slicing makes sure that xml_tools is a list #}
+        {%- set ns.xml_tool_string = ns.xml_tool_string ~ (tool | string) ~ "\n" -%}
+      {%- endfor -%}
+      {%- set xml_tool_string = ns.xml_tool_string + "</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>" -%}
+      {{- xml_tool_string -}}
+    {%- endif -%}
+    {%- if python_tools -%}
+      {%- set ns = namespace(python_tool_string="When you send a message containing Python code between '<code>' and '</code>' tags, it will be executed in a stateful Jupyter notebook environment, and you will then be given the output to continued reasoning in an agentic loop.\n\nYou can use the following tools in your python code like regular functions:\n<tools>\n") -%}
+      {%- for tool in python_tools[:] -%} {# The slicing makes sure that python_tools is a list #}
+        {%- set ns.python_tool_string = ns.python_tool_string ~ (tool | string) ~ "\n" -%}
+      {%- endfor -%}
+      {%- set python_tool_string = ns.python_tool_string + "</tools>\n\nThe state persists between code executions: so variables that you define in one step are still available thereafter." -%}
+      {{- python_tool_string -}}
+    {%- endif -%}
+    {{- "\n\n" -}}
+    {{- "<|im_end|>\n" -}}
+  {%- endif -%}
+{%- endif -%}
+{# ───── main loop ───── #}
+{%- for message in messages -%}
+    {%- set content = message.content if message.content is string else "" -%}
+    {%- if message.role == "user" -%}
+        {{ "<|im_start|>" + message.role + "\n"  + content + "<|im_end|>\n" }}
+    {%- elif message.role == "assistant" -%}
+        {% generation %}
+        {%- if reasoning_mode == "/think" -%}
+            {{ "<|im_start|>assistant\n" + content.lstrip("\n") + "<|im_end|>\n" }}
+        {%- else -%}
+            {{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n" + content.lstrip("\n") + "<|im_end|>\n" }}
+        {%- endif -%}
+        {% endgeneration %}
+    {%- elif message.role == "tool" -%}
+    {{ "<|im_start|>" + "user\n"  + content + "<|im_end|>\n" }}
+    {%- endif -%}
+{%- endfor -%}
+{# ───── generation prompt ───── #}
+{%- if add_generation_prompt -%}
+    {%- if reasoning_mode == "/think" -%}
+        {{ "<|im_start|>assistant\n" }}
+    {%- else -%}
+        {{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n"  }}
+    {%- endif -%}
+{%- endif -%}

config.json

@@ -0,0 +1,111 @@
+{
+  "architectures": [
+    "SmolLM3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 128012,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "layer_types": [
+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "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",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 65536,
+  "max_window_layers": 28,
+  "mlp_bias": false,
+  "model_type": "smollm3",
+  "no_rope_layer_interval": 4,
+  "no_rope_layers": [
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+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 4,
+  "pad_token_id": 128012,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 5000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.8.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128012,
+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.95,
+  "transformers_version": "5.8.0"
+}

mn_context_engine_model_v3.json

@@ -0,0 +1,7 @@
+{
+  "name": "mn-context-engine-model-v3",
+  "base_model": "HuggingFaceTB/SmolLM3-3B",
+  "source_adapter": "artifacts/models/smollm3-context-compressor-v3-dpo",
+  "merge_dtype": "bfloat16",
+  "format": "merged_full_model_safe_tensors"
+}

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tokenizer.json

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+oid sha256:7b6a500b662a34eb3f0374db856ba4ad7de4c81040571d78dc0d357238930005
+size 17208819

tokenizer_config.json

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+}