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

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

Benchmark	Score	Base	Δ	Verified
humaneval	61.0	62.2	-1.2	✅ Result hash
humaneval_plus	53.0	53.7	-0.7	✅ Result hash

What Changed (Base → Forged)

	Base	Forged	Delta
Pruning	None	12% heads (activation-magnitude)	-12% params ✅
compensation-lora	None	rank=16	q_proj, k_proj, v_proj, o_proj...
Pipeline		prune → lora → lora → eval	1 cycles

Runs On

Device	Format	Size	Speed
NVIDIA GeForce RTX 5090	fp16	—	Verified
MacBook Pro 32GB	fp16	8.0GB	Expected
MacBook Air 16GB	Q8_0	~4.0GB	Expected
MacBook Air 8GB	Q4_K_M	~2.5GB	Expected
iPhone / Android	Q4_K_M	~2.5GB	Expected

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.

What	Proof
Model weights	`sha256:156247b9f9b25d302651e2540f1dad58d...`
Forged on	NVIDIA GeForce RTX 5090, ?
Published	huggingface — 2026-04-08T05:02:57.072577+00:00
Trust level	`self-attested`
Spec	ForgeAlloy — Rust/Python/TypeScript

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