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jdecim/
SFT_202112-earnings-sft

Name: SFT_202112-earnings-sft
Availability: InStock

Diamegs/PIT-4B-FT-202112

adapter

mit

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SFT_202112 — LoRA adapter for PIT-4B-FT on earnings-call QA

LoRA adapter trained on top of Diamegs/PIT-4B-FT-202112. Fine-tuned on jdecim/pit-earnings-call-qa for question answering over US earnings-call transcripts, respecting PIT chronological discipline.

Training

Base: Diamegs/PIT-4B-FT-202112
Method: LoRA via 🤗 peft
- r = 16, α = 32, dropout = 0.05
- target_modules = auto
label_shift_mode: auto (PIT models pre-shift labels; do NOT use model mode — it causes identity collapse)
render_format: pit (PIT chat template: <|user|>/<|assistant|>/<|end|>)
Epochs: 1.0
Approx steps: 11046
Effective batch size: 16
LR: 2e-4 (cosine, warmup_ratio=0.03)
Final eval loss: 0.3224

Usage

python

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained(
    "Diamegs/PIT-4B-FT-202112",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
)
tok = AutoTokenizer.from_pretrained("Diamegs/PIT-4B-FT-202112", trust_remote_code=True)
model = PeftModel.from_pretrained(base, "jdecim/SFT_202112-earnings-sft").to("cuda").eval()

prompt = "<|user|>\nQuestion: What was Q4 net revenue?\nContext: …\n<|assistant|>\n"
ids = tok(prompt, return_tensors="pt").to("cuda")
out = model.generate(**ids, max_new_tokens=256, do_sample=False,
                     eos_token_id=tok.encode("<|end|>", add_special_tokens=False)[-1:])
print(tok.decode(out[0, ids.input_ids.shape[1]:], skip_special_tokens=False))

Dataset

jdecim/pit-earnings-call-qa — see that page for the four QA buckets, split sizes, and PIT discipline details. This adapter was trained on the snapshot matching Diamegs/PIT-4B-FT-202112:

python

from datasets import load_dataset
snapshot = "202112"  # the trailing YYYYMM in the base model name
ds = load_dataset("jdecim/pit-earnings-call-qa", snapshot, split="train")

License

MIT.