LOREA-cyber v5.8: Qwen3.6-35B-A3B, trained for restraint. False alarms 32% -> 9%.

.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 +1,154 @@
+---
+language: en
+library_name: mlx
+pipeline_tag: text-generation
+base_model: mlx-community/Qwen3.6-35B-A3B-4bit
+tags:
+- mlx
+- qwen3.6
+- moe
+- cybersecurity
+- security
+- code-review
+- vulnerability-analysis
+license: apache-2.0
+---
+
+# LOREA-cyber v5.8
+
+Qwen3.6-35B-A3B with a LoRA merged in, 4-bit MLX. Mixture of experts, so about 3B parameters are
+active per token and it runs faster than the size suggests. No adapter needed at inference.
+
+It reviews code for security problems. The thing it does differently from the base model is know
+when to stay quiet.
+
+## What changed
+
+The base model is a strong analyst but it over-reports. Shown code that is correctly written, it
+claims a vulnerability about a third of the time. That makes it tiring to use, because you spend
+your time dismissing findings instead of fixing them.
+
+This version was trained mostly to fix that. Roughly a third of the training data is code that is
+correctly implemented, where the right answer is "this is fine, and here is the control that makes
+it fine."
+
+| | base 35B | v5.8 |
+|---|---|---|
+| finds planted bugs | 91% | 84% |
+| false alarms on correct code | 32% | 9% |
+| correctly clears correct code | 32% | 48% |
+
+Seven points of recall for a bit under a quarter of the false alarms.
+
+On a hypothetical 100 files with 10 real bugs in them:
+
+| | bugs found | false alarms | share of findings that are real |
+|---|---|---|---|
+| base 35B | 9.1 | 28.8 | 24% |
+| v5.8 | 8.4 | 5.4 | 61% |
+
+## Benchmarks
+
+| | v5.8 |
+|---|---|
+| SecQA (n=210) | 99.1% |
+| CyberMetric (n=120) | 94.2% |
+| security MCQ, held out (n=150) | 99.3% |
+| MMLU-Pro (n=120) | 55.0% |
+| HumanEval (n=60) | 100% |
+| refuses harmful requests (n=60) | 80% |
+| wrongly refuses legitimate work (n=60) | 0% |
+
+Two things worth saying about these.
+
+The HumanEval number should not be read as "this model is a perfect programmer." HumanEval is old
+and almost certainly in the training data of any 2026 model. Read it as evidence that fine-tuning
+did not damage coding ability, which is the only claim it supports.
+
+The base model was not re-measured on these benchmarks, so the scores cannot be split between what
+the base already did and what the fine-tune added. Most of the general capability is the base. The
+change that is clearly attributable to training is the false-alarm rate, which was measured on both.
+The same caveat applies to the refusal rate: Qwen3.6 already refuses harmful requests to some
+degree, and that portion was not isolated.
+
+## Prompting matters more than you would expect
+
+During evaluation the system prompt changed the false-alarm rate by about six times on the same
+model and the same code:
+
+| prompt | false alarms |
+|---|---|
+| "Identify the security flaw, the invariant it breaks..." | 60% |
+| "Report any issues you can substantiate. If the code is correct, say so." | 10% |
+
+Asking a model to identify the flaw tells it a flaw exists. Give it permission to find nothing.
+
+Suggested system prompt:
+
+```
+You are a security code reviewer. Review the code and report any security issues you can
+substantiate. If the code is correctly implemented, say so plainly.
+```
+
+## Answer style
+
+Answers are shorter than the base model's. It names the bug and usually explains the mechanism, but
+it does not always spell out remediation. If you want a fix every time, ask for one.
+
+Example output:
+
+> The path traversal check is insufficient. It only looks for `..` in the original filename, but
+> traversal can still occur after URL decoding. A filename like `..%2F..` passes the check since it
+> contains `%2F`, not `..`, but after unquoting it becomes `../..`. The check must run on the
+> decoded path.
+
+## Running it
+
+```bash
+python3 -m mlx_lm.chat --model MK4-Research/LOREA-cyber-v5.8
+```
+
+Needs roughly 20 GB of memory at 4-bit.
+
+## Training
+
+LoRA, 1200 iterations, learning rate 2e-5, rank 16, on 5,278 rows.
+
+LoRA was attached only to the attention projections (`q_proj`, `k_proj`, `v_proj`, `o_proj`). This
+matters on a mixture-of-experts model: including the 256 expert MLPs produces 256M trainable
+parameters and runs out of memory immediately on a 32 GB machine. Attention only gives 1.03M
+parameters and fits in about 24 GB.
+
+Other settings: batch size 1, gradient accumulation 2, gradient checkpointing on, sequence length
+256.
+
+Training data is published at `MK4-Research/LOREA-cyber-training-data`. It is decontaminated by
+8-gram overlap against the evaluation sets.
+
+## What did not work
+
+Three earlier attempts fine-tuned a 9B model to be a better analyst. All three failed, and one made
+the model measurably worse than the model it started from. The training data for that attempt was
+generated by a small model and was heavily templated. 99 of 99 rows in one file began with the same
+words, so the model learned the format instead of the reasoning and produced that format filled in
+with wrong content.
+
+What eventually worked was the opposite approach. Rather than trying to teach analysis to a model
+that lacked it, take a model that already analyses well and teach it restraint. Restraint appears to
+be much easier to train than capability.
+
+## Limits
+
+It is a 4-bit quantized model and it will still be wrong sometimes. It misses about one bug in six
+on the internal benchmark. It has not been tested on large real codebases, only on snippets and
+small multi-file examples. Treat its output as a starting point for review, not a verdict.
+
+Intended for authorized security work: reviewing code you own or have permission to assess, CTF
+practice, and teaching.
+
+## Related
+
+- Benchmarks: `MK4-Research/VAB-vulnerability-analysis-benchmark`
+- Training data: `MK4-Research/LOREA-cyber-training-data`
+- Evaluation sets: `MK4-Research/LOREA-cyber-eval`
+- Previous release: `MK4-Research/LOREA-cyber-v5.5` (9B, different base)

chat_template.jinja

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+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + 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" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|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 %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if (preserve_thinking is defined and preserve_thinking is true) or (loop.index0 > ns.last_query_index) %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | string if args_value is string else args_value | tojson | safe %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- 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' }}
+    {%- else %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,757 @@
+{
+    "architectures": [
+        "Qwen3_5MoeForConditionalGeneration"
+    ],
+    "eos_token_id": [
+        248046,
+        248044
+    ],
+    "image_token_id": 248056,
+    "model_type": "qwen3_5_moe",
+    "quantization": {
+        "group_size": 64,
+        "bits": 4,
+        "mode": "affine",
+        "language_model.model.layers.0.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.0.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.1.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.1.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.2.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.2.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.3.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.3.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.4.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.4.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.5.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.5.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.6.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.6.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.7.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.7.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.8.mlp.gate": {
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+            "bits": 8
+        },
+        "language_model.model.layers.8.mlp.shared_expert_gate": {
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+            "bits": 8
+        },
+        "language_model.model.layers.9.mlp.gate": {
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+            "bits": 8
+        },
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+            "bits": 8
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+            "bits": 8
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+            "bits": 8
+        },
+        "language_model.model.layers.11.mlp.gate": {
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+            "bits": 8
+        },
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+            "bits": 8
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+            "bits": 8
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+            "bits": 8
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+            "bits": 8
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+            "bits": 8
+        },
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+            "bits": 8
+        },
+        "language_model.model.layers.25.mlp.gate": {
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+            "bits": 8
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+        "language_model.model.layers.25.mlp.shared_expert_gate": {
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+            "bits": 8
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+        "language_model.model.layers.26.mlp.gate": {
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+            "bits": 8
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+            "bits": 8
+        },
+        "language_model.model.layers.27.mlp.gate": {
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+            "bits": 8
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+        "language_model.model.layers.27.mlp.shared_expert_gate": {
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+            "bits": 8
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+        },
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+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.30.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.30.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.31.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.31.mlp.shared_expert_gate": {
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+            "bits": 8
+        },
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+            "bits": 8
+        },
+        "language_model.model.layers.32.mlp.shared_expert_gate": {
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+        },
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+            "bits": 8
+        },
+        "language_model.model.layers.33.mlp.shared_expert_gate": {
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+            "bits": 8
+        },
+        "language_model.model.layers.34.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.34.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "language_model.model.layers.35.mlp.gate": {
+            "group_size": 64,
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