v2-7b-coder-compensated.alloy.json

{
  "name": "v2-7b-coder-compensated",
  "version": "1.2.1",
  "description": "Methodology validation artifact for the v2 forge pipeline + KL-distillation compensation LoRA. Demonstrates that aggressive head pruning + activation-metric importance + pad-mode defrag, when paired with output-distribution distillation against the unmodified teacher, recovers near-base HumanEval capability (61.0 vs 62.2 base, within calibration tolerance). This is the empirical anchor for PLASTICITY-COMPACTION \u00a74.1.3.3 and the loss-function ablation that closes the \u00a74.1.3.2 PPL/HumanEval disconnect. NOT a Pareto improvement over the unmodified base 7B at any single VRAM tier \u2014 published as proof that the methodology stack works end-to-end, in preparation for the Qwen3.5-35B-A3B and 397B-A17B forges where the pruning dimension actually wins.",
  "author": "continuum-ai",
  "tags": [
    "code",
    "qwen2.5",
    "7b",
    "validation-artifact",
    "forge-alloy",
    "compensation-lora",
    "distillation"
  ],
  "license": "apache-2.0",
  "source": {
    "baseModel": "Qwen/Qwen2.5-Coder-7B",
    "architecture": "qwen2",
    "isMoE": false
  },
  "stages": [
    {
      "type": "prune",
      "strategy": "activation-magnitude",
      "level": 0.125,
      "minHeadsPerLayer": 4,
      "minKvHeadsPerLayer": 2,
      "analysisSteps": 200,
      "perLayerNormalized": true,
      "defragMode": "pad",
      "notes": "Layer-normalized activation-magnitude head importance (PLASTICITY-COMPACTION \u00a74.1.3.1 fix). Pad-mode defrag preserves the q_proj invariant num_q_heads*head_dim==hidden_size so the artifact loads in llama.cpp (Finding 6 fix from VALIDATED-TENSOR-SURGERY)."
    },
    {
      "type": "lora",
      "domain": "code",
      "dataset": "m-a-p/CodeFeedback-Filtered-Instruction",
      "steps": 500,
      "learningRate": "2e-4",
      "batchSize": 4,
      "gradientAccumulation": 4,
      "scheduler": "cosine",
      "precision": "bf16",
      "sequenceLength": 2048,
      "calibrationSource": "code",
      "notes": "Single-cycle code-domain LoRA fine-tuning on the pruned student. 1-cycle ablation chosen because the 3-cycle multi-cycle test surfaced the \u00a74.1.3.2 PPL/HumanEval disconnect (54.9 \u2192 46.3 across cycles)."
    },
    {
      "type": "lora",
      "name": "compensation-lora",
      "domain": "distillation",
      "lossType": "kl_logits",
      "kdTemperature": 2.0,
      "teacher": "Qwen/Qwen2.5-Coder-7B",
      "calibrationDataset": "heldout_mix.jsonl (50 examples: code/math/science/history/multiple-choice, hand-written, disjoint from any benchmark)",
      "steps": 500,
      "learningRate": "1e-4",
      "loraRank": 16,
      "loraAlpha": 32,
      "targetModules": [
        "q_proj",
        "k_proj",
        "v_proj",
        "o_proj",
        "gate_proj",
        "up_proj",
        "down_proj"
      ],
      "trainableParamsPct": 0.527,
      "teacherPrecision": "bnb-8bit",
      "studentPrecision": "fp16-grad-checkpoint",
      "mergedAtSave": true,
      "notes": "PLASTICITY-COMPACTION \u00a74.1.3.3. KL divergence on output logits is the structural fix for the \u00a74.1.3.2 disconnect. Loss-function ablation: MSE-on-hidden-states collapsed the model to 0.0 (degenerate fixed point); KL-on-logits recovered to 61.0. LoRA adapter merged into student weights at save time so inference-time VRAM and tokens/sec are unchanged from the un-compensated student."
    },
    {
      "type": "eval",
      "benchmarks": [
        {
          "name": "humaneval",
          "calibrated": true
        },
        {
          "name": "humaneval_plus",
          "calibrated": true
        }
      ],
      "calibrationAnchor": {
        "model": "Qwen/Qwen2.5-Coder-7B",
        "publishedScore": 61.6,
        "publishedSource": "Qwen2.5-Coder Technical Report Table 5, arXiv:2409.12186",
        "measuredScore": 62.2,
        "delta": 0.6,
        "tolerance": 3.0,
        "passed": true
      },
      "notes": "All HumanEval numbers are anchor-calibrated against the unmodified Qwen2.5-Coder-7B base measured on the same hardware/pipeline in the same run. Hard-fail tolerance: \u00b13.0 points. Anchor delta: +0.6/+0.7 vs Qwen-published 61.6/53.0, deterministic across 6+ independent runs."
    }
  ],
  "cycles": 1,
  "hardware": {
    "minVramGb": 16,
    "recommendedVramGb": 24,
    "deviceTargets": [
      "rtx3090",
      "rtx4090",
      "rtx5090"
    ]
  },
  "results": {
    "baselinePerplexity": null,
    "finalPerplexity": null,
    "improvementPct": null,
    "benchmarks": [
      {
        "name": "humaneval",
        "metric": "pass@1",
        "score": 61.0,
        "baseScore": 62.2,
        "delta": -1.2,
        "calibrated": true,
        "withinCalibrationTolerance": true,
        "samplesPath": "eval/humaneval/humaneval_samples.jsonl",
        "resultHash": "sha256:1d7d6404d962824aae828c3e52395c2298854698668bd11e4a61d63588df030f"
      },
      {
        "name": "humaneval_plus",
        "metric": "pass@1",
        "score": 53.0,
        "baseScore": 53.7,
        "delta": -0.7,
        "calibrated": true,
        "withinCalibrationTolerance": true,
        "samplesPath": "eval/humaneval/humaneval_samples.jsonl",
        "resultHash": "sha256:1d7d6404d962824aae828c3e52395c2298854698668bd11e4a61d63588df030f"
      }
    ],
    "lossFunctionAblation": [
      {
        "lossType": "mse_hidden",
        "humaneval": 0.0,
        "humaneval_plus": 0.0,
        "outcome": "degenerate fixed point \u2014 model collapsed to outputting '0'"
      },
      {
        "lossType": "kl_logits",
        "humaneval": 61.0,
        "humaneval_plus": 53.0,
        "outcome": "near-base recovery within calibration tolerance"
      }
    ],
    "fourRunProgression": [
      {
        "run": 1,
        "config": "broken global-flat L2-weight",
        "humaneval": 50.0
      },
      {
        "run": 2,
        "config": "layer-normalized activation, 1-cycle 500-step",
        "humaneval": 54.9
      },
      {
        "run": 3,
        "config": "layer-normalized activation, 3-cycle (ablation)",
        "humaneval": 46.3
      },
      {
        "run": 4,
        "config": "1-cycle + KL compensation LoRA",
        "humaneval": 61.0
      }
    ],
    "hardwareVerified": [
      {
        "device": "NVIDIA GeForce RTX 5090",
        "vramGb": 32
      }
    ],
    "integrity": {
      "trustLevel": "self-attested",
      "fileHashes": [
        {
          "filename": "model.safetensors",
          "sha256": "5bc5e7f38b8f44152d711cfcfe18710b7800545756b79280dc1166a027d47c50",
          "size": 15231271816
        }
      ],
      "modelHash": "sha256:156247b9f9b25d302651e2540f1dad58d57ffacd8cd43ded17ddaefd16300faf"
    }
  },
  "methodologyPaperUrl": "https://github.com/CambrianTech/continuum/blob/main/docs/papers/PLASTICITY-COMPACTION.md",
  "limitations": [
    "This model is currently a methodology demonstration rather than a Pareto-optimal artifact at any specific hardware tier. For production code workloads on smaller hardware, the unmodified Qwen2.5-Coder-7B at standard quantization (Q4_K_M / Q5_K_M / Q8_0) may be a better fit pending the larger Qwen3.5+ forges that exercise the pruning dimension where this methodology actually wins.",
    "Validated on HumanEval / HumanEval+ for English-language Python code completion. Performance on other programming languages, code paradigms (functional, embedded, kernel), or code-adjacent domains (SQL, regex, shell) has not been measured.",
    "Ships as fp16 only. GGUF quantization tiers (Q5_K_S / Q3_K_M / Q2_K) are not yet published for this artifact; the per-tier comparison from the development log showed base+quant dominates v2+quant at every VRAM tier on the same 7B base, which is why the methodology validation here uses fp16 and the production GGUF publishes are reserved for the Qwen3.5+ forges where the dimension flips.",
    "Vision modality not yet wired in. The Continuum sensory architecture treats vision as first-class for personas, but this 7B coder artifact is text-only."
  ],
  "receipt": {
    "publications": [
      {
        "target": "huggingface",
        "url": "https://huggingface.co/continuum-ai/v2-7b-coder-compensated",
        "publishedAt": "2026-04-08T05:02:57.072577+00:00"
      }
    ],
    "issuedAt": "2026-04-08T05:02:57.072577+00:00"
  }
}