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Model Details
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modrill
Model Tree
Qwen/Qwen3-4B-Base
Input Modalities
Output Modalities
Supported Functionality
A-U025 is the Phase A uniform-scale arm. Starting from the completed source
LoRA, every selected LoRA B tensor—including the lm_head adapter—is
multiplied by 0.25 in FP32. LoRA A tensors are unchanged. With
lora_alpha=128 and r=64, PEFT therefore applies the exact intended
0.25× source delta to all 253 adapted modules.
Because Qwen3 ties lm_head.weight to embed_tokens.weight, the released
standard-PEFT representation stores the head factors as transposed
embed_tokens LoRA factors and sets ensure_weight_tying=true. PEFT then
shares that adapter with the tied output layer, matching both input-embedding
and output-head effects without storing any base-layer tensor.
The repository includes MODULE_SCALE_MANIFEST.json, which records every
logical module, tensor key, physical base weight, and scale. This release is
from the completed Phase A delta-scaling line; it is not the later failed
NEXTGEN route and does not include subsequent protocol-repair experiments.
Use recent transformers and peft versions. Load the fixed base first, then
attach this adapter:
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase_id = "Qwen/Qwen3-4B-Base"base_revision = "906bfd4b4dc7f14ee4320094d8b41684abff8539"adapter_id = "modrill/Qwen3-4B-Base-ThinkCode-A-U025"tokenizer = AutoTokenizer.from_pretrained(base_id, revision=base_revision)base = AutoModelForCausalLM.from_pretrained(base_id,revision=base_revision,torch_dtype="auto",device_map="auto",)model = PeftModel.from_pretrained(base, adapter_id)messages = [{"role": "user", "content": "Write a Python function that checks whether a number is prime."}]prompt = tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True,enable_thinking=False,)inputs = tokenizer(prompt, return_tensors="pt").to(model.device)eos_ids = [tokenizer.eos_token_id,tokenizer.convert_tokens_to_ids("<|im_end|>"),]outputs = model.generate(**inputs, max_new_tokens=2048, eos_token_id=eos_ids)print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
The base tokenizer's chat template supports enable_thinking. Disable it for
direct code generation matching the concise screening style, or enable it when
explicit reasoning is desired. Pass both <|endoftext|> and <|im_end|> as
EOS IDs. Keep the combined prompt and generated sequence within 32K tokens,
the fixed base model configuration limit, unless a separate long-context
extension is validated.
On the corrected EvalScope Full1055 development suite, the preregistered
seed=3407 code_only result was 266/1055 = 25.21%. Independent forward
and reverse scoring produced 0 verdict flips.
This is a single-seed development screening result, not formal
confirmation, a held-out estimate, or a multi-seed aggregate. No aggregate from
A-NH025 is attributed to this adapter.
The fixed base card and included license identify Apache-2.0. This adapter preserves that license text and metadata. Users should independently verify the upstream Qwen3 license, notices, training-data terms, and applicability to their use case.