12% Pruned, 61.0 HUMANEVAL (base 62.2)

Qwen2.5-Coder-7B recovered to within calibration tolerance of the unmodified base via KL-distillation compensation LoRA.

• HUMANEVAL: 61.0 (base 62.2, Δ -1.2)
• HUMANEVAL+PLUS: 53.0 (base 53.7, Δ -0.7)

Every claim on this card is verified
Trust: self-attested · 2 benchmarks · 1 device tested
ForgeAlloy chain of custody · Download alloy · Merkle-chained

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Qwen2.5-Coder-7B with cryptographic provenance via the ForgeAlloy chain of custody. Scores 61.0 humaneval against the unmodified base's 62.2, recovered to within calibration tolerance after head pruning + distillation. Ships with the per-problem evaluation outputs so the score is independently verifiable.

Benchmarks

What Changed (Base → Forged)

Runs On

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("continuum-ai/v2-7b-coder-compensated",
    torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("continuum-ai/v2-7b-coder-compensated")

inputs = tokenizer("def merge_sort(arr):", return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Methodology

Produced via head pruning, LoRA fine-tuning, KL-distillation compensation against the unmodified teacher. Full methodology, ablations, and per-stage rationale are in the methodology paper and the companion MODEL_METHODOLOGY.md in this repository. The pipeline ran as prune → lora → lora → eval over 1 cycle on NVIDIA GeForce RTX 5090.

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.

Chain of Custody

Scan the QR or verify online. Download the alloy file to verify independently.

Make Your Own

Forged with Continuum — a distributed AI world that runs on your hardware.

The Factory configurator lets you design and forge custom models visually — context extension, pruning, LoRA, quantization, vision/audio modalities. Pick your target devices, the system figures out what fits.

GitHub · All Models · Forge-Alloy

License

apache-2.0