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hxgdzyuyi/qwen3-8b-steam-entity-linking
qwen3-8b-steam-entity-linking is a machine learning model from hxgdzyuyi. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
This adapter maps Steam game names and related expressions to one-token labels such as <GAME730. It must be loaded with the tokenizer in this repository and the pinned base-model revision below.
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From the Hugging Face model README
This adapter maps Steam game names and related expressions to one-token labels such as <GAME_730>. It must be loaded with the tokenizer in this repository and the pinned base-model revision below.
Qwen/Qwen3-8B-Base49e3418fbbbca6ecbdf9608b4d22e5a407081db4c4d33e6e7c435947e5856bd723814795b067404chttps://github.com/hxgdzyuyi/qwen-steam-entity-linking.git| Epoch | Canonical entity top-1 | Held-out alias entity top-1 |
|---|---|---|
| 2 | 99.55% | 54.62% |
| 4 | 99.80% | 53.12% |
| 6 | 100.00% | 55.57% |
| 8 | 100.00% | 55.98% |
| 10 | 100.00% | 55.98% |
Canonical accuracy covers every entity across every configured prompt. Alias accuracy covers 184 frozen inputs across the same 4 prompts (736 rows) and uses entity-constrained top-1. The selected checkpoint is the alias-best checkpoint among those reaching the canonical threshold; publication additionally requires the alias threshold.
Load the tokenizer from this repository, resize Qwen/Qwen3-8B-Base to the tokenizer length, and then load the PEFT adapter. Do not use the base tokenizer without the added <GAME_APPID> tokens.
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
adapter_id = "hxgdzyuyi/qwen3-8b-steam-entity-linking"
tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen3-8B-Base", revision="49e3418fbbbca6ecbdf9608b4d22e5a407081db4"
)
base.resize_token_embeddings(len(tokenizer))
model = PeftModel.from_pretrained(base, adapter_id)
# Score one controlled prompt and choose only among registered entity labels.
prompt = "游戏信息:CS2\nSteam AppID:"
inputs = tokenizer(prompt, return_tensors="pt")
entity_ids = tokenizer.convert_tokens_to_ids(
tokenizer.additional_special_tokens
)
logits = model(**inputs).logits[0, -1, entity_ids]
predicted_id = entity_ids[int(logits.argmax())]
print(tokenizer.convert_ids_to_tokens(predicted_id))
The latest 900 games may not be represented in the base model's pretraining knowledge, so alias and natural-language generalization can be weaker for recent releases. This is a memorization/generalization PoC, not a replacement for a retrieval-backed production entity linker. Steam catalog names and AppIDs can also change over time.