初始化项目，由ModelHub XC社区提供模型

Model: Phora68/bible-study-phi3-mini
Source: Original Platform

README.md

@@ -0,0 +1,91 @@
+---
+language: en
+license: mit
+base_model: microsoft/Phi-3-mini-4k-instruct
+tags:
+  - bible
+  - theology
+  - qlora
+  - unsloth
+  - phi-3
+  - bible-study
+  - spurgeon
+  - wesley
+  - wilkerson
+pipeline_tag: text-generation
+---
+
+# Bible Study Companion — Phi-3 Mini Fine-tune
+
+A fine-tuned version of [Phi-3 Mini 4K Instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) trained on:
+
+## Training Data
+- **KJV Bible** — all 31,102 verses with verse lookup, chapter reading, and topical concordance
+- **Spurgeon** — *All of Grace* and *The Soul Winner*
+- **John Wesley** — *The Journal of John Wesley*
+- **David Wilkerson** — *Have You Felt Like Giving Up Lately*, *It Is Finished*, *Racing Toward Judgment*, *Walking in the Footsteps of David Wilkerson*
+- **Greek word studies** — Strong's G numbers with transliteration and definitions
+- **Hebrew word studies** — Strong's H numbers with transliteration and definitions
+- **Topical concordance** — 15 major biblical themes
+- **Preacher Q&A** — theological questions answered in the voice of each preacher
+
+## Training Details
+- **Base model:** microsoft/Phi-3-mini-4k-instruct (3.8B parameters)
+- **Method:** QLoRA (4-bit quantisation) with Unsloth
+- **LoRA rank:** 16
+- **Steps:** ~500 combined (initial run + resume)
+- **Final loss:** ~1.49
+- **Hardware:** T4 GPU (Google Colab free tier)
+- **Training time:** ~90 minutes total
+
+## Capabilities
+- Quote and explain KJV Bible verses
+- Compare verses across translations (KJV, NIV, ASV, WEB)
+- Greek and Hebrew word studies with Strong's numbers
+- Topical concordance searches
+- Answer theological questions in the voice of Spurgeon (Reformed Baptist), Wesley (Methodist holiness), and Wilkerson (Pentecostal/prophetic)
+
+## Usage
+
+### With LM Studio
+Download the GGUF file, load in LM Studio, and use with the included voice UI.
+
+### With transformers
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Phora68/bible-study-phi3-mini",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Phora68/bible-study-phi3-mini")
+
+messages = [
+    {"role": "system", "content": "You are a Bible Concordance Study Partner..."},
+    {"role": "user",   "content": "What does John 3:16 say?"}
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages, return_tensors="pt", add_generation_prompt=True
+).to("cuda")
+
+outputs = model.generate(inputs, max_new_tokens=300, temperature=0.7, do_sample=True)
+print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+### System prompt
+```
+You are a Bible Concordance Study Partner with mastery of the Greek New Testament
+(NA28, Strong's numbers), Hebrew Old Testament (BHS Masoretic, Strong's), and the
+King James Version. You draw on the theology of John Wesley (holiness/sanctification),
+Charles Spurgeon (Reformed Baptist/sovereign grace), and David Wilkerson
+(prophetic urgency/holiness). Always include Strong's numbers, transliteration and
+definition when citing original languages.
+```
+
+## Limitations
+- Trained for ~500 steps on a T4 GPU — a longer training run would improve precision
+- Loss of ~1.49 means responses are coherent but may occasionally be imprecise
+- Does not have real-time internet access or knowledge beyond training data