crane-30b

Name: crane-30b
Availability: InStock

crane-30b is a CRANE merge: it is produced by merging two Qwen3-30B-A3B checkpoints (an Instruct base and a Thinking donor) with the CRANE method — it is not trained or fine-tuned from scratch. CRANE (Constrained Reasoning Injection for Code Agents via Nullspace Editing) injects reasoning ability from the Thinking donor into the tool-disciplined Instruct / code base while preserving the base model's output format and tool-calling behavior.

Project page: https://rpi-nsl.github.io/CRANE/ · Code: github.com/rpi-nsl/CRANE

Note: this is the CRANE weight-merging method for code agents. It is unrelated to the similarly-named "CRANE: Reasoning with constrained LLM generation" (arXiv 2502.09061), despite the shared acronym.

How it was made (CRANE)

CRANE is a training-free, parameter-editing weight merge that injects reasoning ability from a "Thinking" donor into a tool-disciplined Instruct / code base, while constraining the edit so the base model's output format and tool-calling behavior are preserved. It treats the Thinking − Instruct delta $\delta = \theta_{\text{think}} - \theta_{\text{inst}}$ as a pool of candidate reasoning edits, and applies three composable stages per layer $l$ and parameter component $c$:

$$ \theta_{\text{merged}}^{(l,c)} = \theta_{\text{inst}}^{(l,c)} + \underbrace{\Pi_{\tau,,q(l,c)}^{\text{GSP}}}{\text{Stage 3}}!\Big( \alpha \cdot \underbrace{S{\text{CTG}}(c,l)}{\text{Stage 2}} \cdot \underbrace{T\big(\delta^{(l,c)}\big)}{\text{Stage 1}} \Big) $$

Three small calibration sets drive the stages — $\mathcal{D}_R$ (reasoning transfer), $\mathcal{D}_A$ (agent-behavior / tool-use preservation), and $\mathcal{D}_F$ (format preservation):

Stage 1 — Magnitude thresholding $T(\delta)$. A deterministic median-magnitude threshold keeps only the larger (top-half) delta coordinates and rescales them by 2, discarding low-confidence noise.
Stage 2 — Conservative Taylor Gate $S_{\text{CTG}}$. From a signed, direction-aware score $s_K(j) = -g_{K,j},\delta_j$ per calibration loss, CTG keeps the positive part of the per-coordinate minimum over the reasoning and agent-behavior objectives, $p_j = [\min{s_R(j), s_A(j)}]+$ — rewarding a coordinate only when the edit helps both. These aggregate into the per-component, per-layer coefficient $S{\text{CTG}}(c,l)$, scaled by the single global merge strength $\alpha$.
Stage 3 — Graduated Sigmoidal Projection (GSP). From the SVD of format-critical Instruct activations $H_q = U_q\Sigma_q V_q^{\top}$, a smooth sigmoidal weight $\mathbf{w}q$ (set by singular amplitude and threshold $\tau$) gives the projector $\Pi{\tau,q}^{\text{GSP}}(\Delta_q) = \Delta_q - \Delta_q V_q \operatorname{diag}(\mathbf{w}_q) V_q^{\top}$, attenuating high-amplitude format directions so reasoning is injected without perturbing chat-template tokens, tool-call delimiters, or JSON/schema structure.

The result is a merge that gains planning / reflection / recovery reasoning while keeping the base agent's compact, tool-call-disciplined behavior — the entire merge is a closed-form edit of the Instruct weights, with no fine-tuning.

This checkpoint's recipe

This checkpoint merges Qwen/Qwen3-30B-A3B-Instruct-2507 (base) and Qwen/Qwen3-30B-A3B-Thinking-2507 (donor) with:

Global injection strength — $\alpha = 0.25$, multiplied by the per-component CTG coefficients, so the Thinking delta is added at roughly a quarter strength.
Per-layer / per-component gating — attention, expert (FFN), norm, and router components each get their own $S_{\text{CTG}}(c,l)$ coefficient, varying by layer index rather than a single flat scalar.
GSP projector — a freshly rebuilt Qwen3-30B graduated-sigmoidal projector (sigmoid threshold $\tau = 0.03$) protects the format / tool-call subspace before injection.

Architecture

The merge preserves the standard Qwen3-30B-A3B (MoE) topology unchanged:

Property	Value
model_type	qwen3_moe
Architecture class	Qwen3MoeForCausalLM
Total params	~30B
Active params	~3B
hidden_size	2048
num_hidden_layers	48
num_experts	128
num_experts_per_tok	8
num_attention_heads	32
num_key_value_heads	4
head_dim	128
moe_intermediate_size	768
max_position_embeddings	262144
vocab_size	151936
dtype	bfloat16
rope_theta	10000000

A config_1m.json is also included for the extended long-context variant: it keeps the same rope_scaling (null) and max_position_embeddings (262144), but adds a dual_chunk_attention_config (dual chunk attention, original_max_position_embeddings = 131072 + sparse-attention settings) for longer-context inference.

Usage

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "zmzfpc/crane-30b"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "user", "content": "Write a Python function that returns the nth Fibonacci number."},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Requires a recent transformers with Qwen3-MoE support (transformers >= 4.51).

Citation / attribution

If you use this model or the CRANE method, please cite:

bibtex

@misc{zhu2026crane,
  title        = {CRANE: Constrained Reasoning Injection for Code Agents via Nullspace Editing},
  author       = {Zhu, Mingzhi and Merler, Michele and Pavuluri, Raju and Patterson, Stacy},
  year         = {2026},
  eprint       = {2605.14084},
  archivePrefix= {arXiv},
  primaryClass = {cs.SE},
  url          = {https://arxiv.org/abs/2605.14084}
}

Project page: https://rpi-nsl.github.io/CRANE/ · Code: github.com/rpi-nsl/CRANE

Base models — built from two Apache-2.0 checkpoints:

Qwen/Qwen3-30B-A3B-Instruct-2507 (base / backbone)
Qwen/Qwen3-30B-A3B-Thinking-2507 (reasoning donor)

License: Apache-2.0 (consistent with both base models and the CRANE code).