Unverified50% confidenceBenchmarkExact time
The author benchmarked on 3 synthetic receipts, achieving 100% accuracy on all four items—date, merchant, total, and tax—with an average of 2.32 seconds per receipt, on hardware consisting of an RTX 4050 laptop GPU (6GB VRAM), a Ryzen 7 7840HS, and 16GB of RAM.
1
Sources
50%
Confidence
Short-term (~14 days)
Relevance
10/18/2026
First Seen
Valid until: 11/1/2026
Sources
Related Entities
Related Claims
UnverifiedIn benchmark testing on an AMD RX 7900 GPU, Ornith 35B achieved an overall accuracy of 85% (18 out of 21 passed), a generation speed of 74 tokens/s, a prompt processing speed of 850 tokens/s, and a VRAM usage of 20 GB.73% similarUnverified文章称IQ3_S档位通常能将准确率保留提升到90%以上、体积约13-15GB,Q4_K_M能保留95%以上准确率、体积约16-18GB71% similarUnverifiedIn the test, model loading took only a few seconds, GPU usage dropped from 23GB to about 17GB, the first test prompt processing took 225ms, generation speed was stable at 33.5 tok/s, with a total output of 1500 tokens.70% similarUnverified开发者公布在RTX 3050 Laptop(4GB)上微调Llama-3.1-8B的实测结果为训练速度119.6 tok/s,峰值显存3.32 GB69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1064401API
curl https://kongchang.com/api/v1/knowledge/claims/1064401MCP
get_claim(id=1064401)