初始化项目，由ModelHub XC社区提供模型

Model: Garv98/GX-Coder-7B
Source: Original Platform

README.md

@@ -0,0 +1,63 @@
+---
+license: apache-2.0
+base_model: unsloth/Qwen2.5-Coder-7B-Instruct
+tags:
+  - code
+  - qlora
+  - unsloth
+  - qwen2.5-coder
+  - text-generation
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# GX-Coder-7B
+
+**v0.1** — QLoRA fine-tune of [`unsloth/Qwen2.5-Coder-7B-Instruct`](https://huggingface.co/Qwen2.5-Coder-7B-Instruct),
+built to power a multi-mode coding agent. This first release sits at **parity with
+the base** on HumanEval+ (see table); it's an end-to-end QLoRA→eval→ship baseline.
+Later versions target agent-specific tasks (tool-calling, web/UI codegen) where
+the base isn't already saturated.
+
+## Summary
+
+- **Base:** unsloth/Qwen2.5-Coder-7B-Instruct
+- **Method:** 4-bit QLoRA (LoRA r=16) via [Unsloth](https://github.com/unslothai/unsloth)
+- **Hardware:** single free-Colab T4 (16GB)
+- **Data:** open instruction-coding datasets, ChatML-formatted (see training repo)
+
+## Benchmarks (HumanEval+ pass@1, greedy)
+
+| Model | HumanEval+ pass@1 |
+|-------|-------------------|
+| unsloth/Qwen2.5-Coder-7B-Instruct (base) | 80.5 |
+| **GX-Coder-7B (this)** | **78.0** |
+
+> Scored with [EvalPlus](https://github.com/evalplus/evalplus) (164 problems,
+> greedy). The ~2pt gap is within noise (~3-4 problems) — treat as parity. A
+> generic fine-tune can't easily beat a near-ceiling base here; gains come from
+> task-specific data in later versions.
+
+## Intended use
+
+Code generation, completion, review, and tool-using agent workflows. Powers the
+companion multi-mode coding agent (Claude-Code-like coder + Figma-like web/UI
+design mode).
+
+## Limitations
+
+Small model fine-tune — not frontier-level. May hallucinate APIs, miss edge
+cases, and produce insecure code. Always review generated code before running.
+Not trained on benchmark test sets (contamination guard in data_prep.py).
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tok = AutoTokenizer.from_pretrained("Garv98/GX-Coder-7B")
+model = AutoModelForCausalLM.from_pretrained("Garv98/GX-Coder-7B", device_map="auto")
+msgs = [{"role": "user", "content": "Write a Python function to check if a string is a palindrome."}]
+ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
+print(tok.decode(model.generate(ids, max_new_tokens=256)[0][ids.shape[1]:], skip_special_tokens=True))
+```