GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
cometadata
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
cometadata
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
987-prompt LaTeX-extracted test split. Matching uses case-insensitive name
matching (fuzz.ratio, threshold 85) and token_sort_ratio + domain
normalization (expand abbreviations, drop postal codes) for affiliations
(threshold 85, audited at precision 1.0 on a 64-pair labeled set so the
metric does not credit genuinely-different institutions).
(format + author_IoU + affiliation_IoU) / 3)| Stage | Test reward |
|---|---|
| Stage 1 only (distil) | 0.918 |
| Stage 1 + Stage 2 (this adapter) | 0.921 |
Pooled (micro) TP/FP/FN across all 987 prompts. Parse rate (schema-valid JSON emitted): 0.992 for both stages.
Authors — fuzzy name match across each prompt's gold vs. predicted author list.
| Stage | TP | FP | FN | P | R | F0.5 | F1 |
|---|---|---|---|---|---|---|---|
| Stage 1 only | 3095 | 147 | 680 | 0.955 | 0.820 | 0.924 | 0.882 |
| Stage 1 + 2 | 3008 | 90 | 767 | 0.971 | 0.797 | 0.930 | 0.875 |
Affiliations — affiliation matching within matched-author pairs; gold affiliations of unmatched authors count as FN, predicted of unmatched as FP.
| Stage | TP | FP | FN | P | R | F0.5 | F1 |
|---|---|---|---|---|---|---|---|
| Stage 1 only | 2821 | 596 | 1348 | 0.826 | 0.677 | 0.791 | 0.744 |
| Stage 1 + 2 | 2793 | 361 | 1376 | 0.886 | 0.670 | 0.832 | 0.763 |
Macro (mean of per-prompt P/R/F) for Stage 1 + 2 is higher because large multi-author papers drag the micro denominators down: authors P 0.954 / R 0.954 / F0.5 0.953 / F1 0.953; affiliations P 0.840 / R 0.822 / F0.5 0.833 / F1 0.826.
DAPO moves precision, not recall — authors FP halves (147 → 90), affiliations FP drops 39% (596 → 361). The model learned to stop emitting hallucinated / wrong items. Recall is flat — the unreachable items are ~4% truly bad data (the author block was lost during source extraction for those papers) plus very large author lists where some authors are consistently skipped.
A discovery during training was that the naive case-sensitive fuzz.ratio
metric was scoring ~22% of correctly extracted papers as 0 — gold labels
are often ALL-CAPS (LUKASZ PAWELEC) while a correct extraction from the
paper text is mixed-case (Łukasz Pawelec); the corrected metric reveals
the model was always ~0.91, not the 0.81 that the buggy metric showed.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B", torch_dtype="bfloat16")tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")model = PeftModel.from_pretrained( base, "cometadata/affiliation-parsing-lora-Qwen3-8B-distil-GLM_4.5_Air-dapo-latex",) SYSTEM = ( "You are an expert at reading academic articles and parsing information " "about their affiliations. The user will show you an academic article and " "your job is to extract the authors and their affiliations in a structured " "format (a JSON array of {name, affiliations}). Respond after </think>.") messages = [ {"role": "system", "content": SYSTEM}, {"role": "user", "content": "<the paper text>"},]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(inputs, max_new_tokens=2048, temperature=0.7, do_sample=True)print(tok.decode(out[0, inputs.shape[1]:], skip_special_tokens=True))The model emits <think>…</think> reasoning followed by a JSON array
[{"name": ..., "affiliations": [...]}, ...].
github.com/cometadata/affiliation-parsing-cl-latex (or the
project directory /scratch/m000152-pm05/affiliation-parsing-cl-latex/).
Apache-2.0 (matches the Qwen3-8B base).
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B", torch_dtype="bfloat16")tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")model = PeftModel.from_pretrained( base, "cometadata/affiliation-parsing-lora-Qwen3-8B-distil-GLM_4.5_Air-dapo-latex",) SYSTEM = ( "You are an expert at reading academic articles and parsing information " "about their affiliations. The user will show you an academic article and " "your job is to extract the authors and their affiliations in a structured " "format (a JSON array of {name, affiliations}). Respond after </think>.") messages = [ {"role": "system", "content": SYSTEM}, {"role": "user", "content": "<the paper text>"},]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(inputs, max_new_tokens=2048, temperature=0.7, do_sample=True)print(tok.decode(out[0, inputs.shape[1]:], skip_special_tokens=True))