初始化项目，由ModelHub XC社区提供模型

Model: RthItalia/NanoLLM-Qwen2.5-7B-v3.1
Source: Original Platform

README.md

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+---
+language:
+  - en
+  - zh
+  - it
+license: other
+tags:
+  - quantization
+  - qwen
+  - qwen2.5
+  - mixed-precision
+  - inference
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# NanoLLM Qwen v3.1
+
+NanoLLM v3.1 artifacts are compact overlay artifacts for Qwen2.5 models. The loader starts from the base model in bitsandbytes 8-bit mode, then replaces the modules that passed the NanoLLM cascade with `TrueQuantLinear` modules.
+
+## Validated Artifacts
+
+| Model | Artifact | Zip size | Gate | Avg cosine | Min cosine | Locked / 8-bit pending |
+| --- | --- | ---: | --- | ---: | ---: | ---: |
+| Qwen2.5-3B-Instruct | `final_artifact_3B.zip` | 799,189,680 bytes | PASS | 0.990625 | 0.984375 | 143 / 109 |
+| Qwen2.5-7B-Instruct | `final_artifact_7B.zip` | 891,419,698 bytes | PASS | 0.990625 | 0.98046875 | 66 / 130 |
+| Qwen2.5-14B-Instruct | `final_artifact_Qwen2.5-14B-Instruct_pruned_pass.zip` | 1,482,019,132 bytes | PASS | 0.990625 | 0.98046875 | 76 / 260 |
+
+The current release gate checks average next-token-logit cosine similarity against the 8-bit reference: `avg >= 0.99`. Minimum cosine is reported as a diagnostic.
+
+## Quick Start
+
+```python
+from load_artifact import load_artifact
+
+model, tokenizer, spec = load_artifact("final_artifact_Qwen2.5-14B-Instruct")
+prompt = "Write a Python function to sort a list using bubble sort."
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=160, do_sample=False)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+Requirements:
+
+```bash
+pip install torch transformers accelerate bitsandbytes safetensors
+```
+
+## Runtime Notes
+
+- `build_reference_mode`: `8bit`
+- `reference_scope`: `original_baseline`
+- `pending_policy`: `leave_in_base_8bit`
+- `NANO_LOAD_4BIT=1` can be used experimentally to load the base model in 4-bit, but the release tests use 8-bit.
+
+## License
+
+The NanoLLM quantization pipeline is proprietary/internal. Generated artifacts are published for research and evaluation subject to the repository license terms.