README.md

metadata

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)

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

License

apache-2.0