--- tags: - 7b - agentic-coding - android - apple-silicon - attested - bash - c - chain-of-custody - chinese - code - code-completion - code-generation - code-infill - compacted - compensation-lora - consumer-gpu - cpp - cryptographically-verified - css - distillation - edge-inference - efficient - embedded - english - forge-alloy - function-calling - general - general-purpose - go - head-pruning - html - iphone - java - javascript - knowledge-distillation - kotlin - llama-cpp - lm-studio - local-inference - lora - macbook - mlx - mobile - multilingual - ollama - on-device - optimized - php - pruned - python - qwen - qwen-coder - qwen2 - qwen2.5 - qwen2.5-coder - raspberry-pi - reproducible - ruby - rust - sql - swift - teacher-student - text-generation - typescript - validation-artifact - versatile base_model: Qwen/Qwen2.5-Coder-7B pipeline_tag: text-generation license: apache-2.0 --- # 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)

Verify Chain of Custody

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](https://github.com/CambrianTech/forge-alloy) 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 ```python 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](https://github.com/CambrianTech/continuum/blob/main/docs/papers/PLASTICITY-COMPACTION.md) and the companion [`MODEL_METHODOLOGY.md`](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](https://cambriantech.github.io/forge-alloy/verify/#4fe422e9b01fa8f0). Download the [alloy file](v2-7b-coder-compensated.alloy.json) to verify independently. | What | Proof | |------|-------| | Model weights | `sha256:156247b9f9b25d302651e2540f1dad58d...` | | Forged on | NVIDIA GeForce RTX 5090, ? | | Published | [huggingface](https://huggingface.co/continuum-ai/v2-7b-coder-compensated) — 2026-04-08T05:02:57.072577+00:00 | | Trust level | [`self-attested`](https://github.com/CambrianTech/forge-alloy/blob/main/docs/ATTESTATION.md) | | Spec | [ForgeAlloy](https://github.com/CambrianTech/forge-alloy) — Rust/Python/TypeScript | ## Make Your Own Forged with [Continuum](https://github.com/CambrianTech/continuum) — a distributed AI world that runs on your hardware.

Continuum Model Factory

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](https://github.com/CambrianTech/continuum) · [All Models](https://huggingface.co/continuum-ai) · [Forge-Alloy](https://github.com/CambrianTech/forge-alloy) ## License apache-2.0