初始化项目，由ModelHub XC社区提供模型

Model: North-ML1/Aurora-One-Mini
Source: Original Platform

README.md

@@ -0,0 +1,111 @@
+---
+language:
+- en
+tags:
+- causal-lm
+- text-generation
+- gpt2
+- small-language-model
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Aurora One Mini — 124M
+
+Aurora One Mini is a compact, community-built language model designed for fast local chat, experiments, and lightweight AI applications.
+
+At only **124 million parameters**, it is small enough to run comfortably on ordinary laptops and edge devices while remaining useful for short-form generation and experimentation.
+
+## What makes it interesting
+
+- **Tiny and fast:** practical for local inference and rapid prototyping
+- **Native ChatML format:** structured user/assistant conversations
+- **Hugging Face + GGUF exports:** works with Transformers and llama.cpp-compatible tools
+- **Open experiment:** trained and evaluated on a single consumer GPU
+
+## Model details
+
+- Architecture: GPT-style causal language model
+- Parameters: approximately 124M
+- Layers: 12
+- Hidden size: 768
+- Attention heads: 12
+- Context length: 1,024 tokens
+- Vocabulary: GPT-2 BPE plus ChatML control tokens
+- Final pretraining: 45,000 steps, approximately 15 tokens per parameter
+- Released checkpoint: deterministic v2, step 2,000 of targeted post-training
+
+## Quick start
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "North-ML1/Aurora-One-Mini"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+prompt = "What is the capital of France?"
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+inputs = tokenizer(text, return_tensors="pt")
+
+with torch.no_grad():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=80,
+        temperature=0.7,
+        top_p=0.9,
+        do_sample=True,
+    )
+
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+## GGUF files
+
+The companion GGUF files are provided for local runtimes:
+
+- `aurora_one_mini_deterministic_v2_f16.gguf` — highest fidelity
+- `aurora_one_mini_deterministic_v2_q4_k_m.gguf` — compact CPU-friendly quantization
+
+Use the Q4_K_M file for a fast, low-memory demo. Use the F16 file when preserving maximum quality is more important.
+
+## Honest limitations
+
+This is an experimental 124M model, not a frontier assistant. It can produce fluent short responses, but it may hallucinate, repeat itself, or answer arithmetic and factual questions incorrectly. For dependable applications, pair it with a calculator, retrieval system, memory layer, and explicit output validation.
+
+The native-ChatML factual smoke test scored **3/20** on a small internal suite. This score is reported to set realistic expectations and should not be interpreted as a general benchmark.
+
+## Intended use
+
+Good fits include:
+
+- local chat experiments
+- educational model training projects
+- embedded or low-resource inference
+- prompt-format and agent-runtime experiments
+- fast prototyping with Transformers or llama.cpp
+
+Avoid using it as the sole source of truth for medical, legal, financial, safety-critical, or factual decision-making.
+
+## Prompt format
+
+The model was post-trained using ChatML-style turns:
+
+```text
+<|im_start|><|user|>Your question<|im_end|>
+<|im_start|><|assistant|>
+```
+
+The included tokenizer metadata contains the required special tokens.
+
+## Acknowledgements
+
+Aurora One Mini was trained as a small-scale independent experiment using PyTorch and a consumer NVIDIA GPU. Contributions, evaluations, and improvements are welcome.
+
+## License
+
+Released for research and experimentation. Add the project’s final license here before redistributing commercially.