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vankoha/pandascope-luangiai-v8-gguf
pandascope-luangiai-v8-gguf is a machine learning model from vankoha. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
A QLoRA fine-tune of Qwen/Qwen2.5-7B-Instruct for generating Vietnamese Tử Vi Đẩu Số (Chinese/Vietnamese astrology) chart readings, from the PandaScope project (self-hosted Tử Vi web app). Quantized to Q4KM (~4.4GB) f…
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From the Hugging Face model README
A QLoRA fine-tune of Qwen/Qwen2.5-7B-Instruct for generating Vietnamese Tử Vi Đẩu Số (Chinese/Vietnamese astrology) chart readings, from the PandaScope project (self-hosted Tử Vi web app). Quantized to Q4_K_M (~4.4GB) for easy local use with Ollama:
ollama run hf.co/vankoha/pandascope-luangiai-v8-gguf
Trained on 300 template-drafted (not expert-edited) example readings —
see the project's training/LEDGER.md for the full training log. It
failed its own 0.98 grounding-accuracy gate at 0.9726 on a 12-case
benchmark (occasionally states a star/position not actually present in
the input chart). Not suitable for serving real astrological
decisions — this is shared for the PandaScope app's own local/hobby
self-hosting, where the app's own verify.py grounding checker catches
and falls back on ungrounded output before showing it to a user. Don't
use this checkpoint's output as a standalone source of truth.
It's also only been trained on the one-shot narrative reading format, not
on tool-calling — served through plain Ollama, the chat/tool-calling
feature can occasionally misfire (rare). PandaScope's own backend/serving/
setup serves this same checkpoint through vLLM instead, which fixes that
specific issue (see that project's backend/serving/README.md) — this
GGUF build is for the simpler, zero-GPU-required local Ollama path.
pandascope-luangiai-v8-Q4_K_M.gguf — the merged (base + LoRA) model, quantized.template / params — Ollama chat template + stop-token config (ChatML), tested against this exact checkpoint.Apache 2.0, inherited from the Qwen2.5-7B-Instruct base model.