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ichetandhembre
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Model Details
Model Provider
ichetandhembre
Model Tree
Qwen/Qwen3-4B-Instruct-2507
Input Modalities
Output Modalities
Supported Functionality
Greedy exact-match on a 5,914-row held-out split (provably disjoint from training by input):
| Model | Exact-match | Relaxed-match | mean_reward* |
|---|---|---|---|
| Base Qwen3-4B-Instruct-2507 | 0.409 | 0.577 | 0.472 |
| This adapter (SFT) | 0.932 | 0.976 | 0.939 |
*mean_reward is the normalized reward (exact·1.0 + pii_count·0.5 + format·0.1) / 1.6 (max 1.0).
The Prime Intellect verifier for this task reports the same three components un-normalized
(max 1.6), so e.g. a Prime reward of ~1.50 corresponds to ~0.94 here.
Format compliance is already ~1.00 on the base model; the adapter's gain is almost entirely
learning the exact [PII] segmentation convention (it cuts the relaxed-minus-exact gap from
0.168 to 0.045).
AdamLucek/open-pii-masking-en-us-30k (train split).The published adapter was extracted from the merged SFT checkpoint by a per-layer rank-8 SVD of
(W_merged − W_base). Base + this adapter reproduces the merged model's held-out exact-match to
within bf16 rounding (0.9319 vs 0.9325).
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen3-4B-Instruct-2507", torch_dtype="bfloat16", device_map="auto")model = PeftModel.from_pretrained(base, "ichetandhembre/Qwen3-4B-Instruct-2507-PII-SFT-LoRA")tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") SYSTEM = ("Replace all personally identifiable information (PII) in the text with [PII] tags. " "PII includes: names, dates, phone numbers, SSNs, account numbers, addresses, " "email addresses. Wrap the masked text in <masked_output>...</masked_output>.")msgs = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": "Hi, this is John Smith, call me at 555-0123."}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)print(tok.decode(model.generate(ids, max_new_tokens=384, do_sample=False)[0][ids.shape[1]:], skip_special_tokens=True))Or serve base + adapter with vLLM (enable_lora=True, max_lora_rank>=8).
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen3-4B-Instruct-2507", torch_dtype="bfloat16", device_map="auto")model = PeftModel.from_pretrained(base, "ichetandhembre/Qwen3-4B-Instruct-2507-PII-SFT-LoRA")tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") SYSTEM = ("Replace all personally identifiable information (PII) in the text with [PII] tags. " "PII includes: names, dates, phone numbers, SSNs, account numbers, addresses, " "email addresses. Wrap the masked text in <masked_output>...</masked_output>.")msgs = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": "Hi, this is John Smith, call me at 555-0123."}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)print(tok.decode(model.generate(ids, max_new_tokens=384, do_sample=False)[0][ids.shape[1]:], skip_special_tokens=True))