初始化项目，由ModelHub XC社区提供模型

Model: KookiesXy/Neo50M
Source: Original Platform

README.md

@@ -0,0 +1,72 @@
+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- llama
+- causal-lm
+- chat
+- tiny-language-model
+- neo50m
+---
+
+# Neo50M
+
+Neo50M is a tiny decoder-only chat language model trained from scratch. It is designed for toy/local assistant use, educational experiments, lightweight generation, and testing training pipelines.
+
+## Model Details
+
+- **Type:** decoder-only causal language model, Llama-compatible architecture
+- **Parameters:** approximately 52.6M
+- **Context length target:** 16k tokens
+- **Training target:** about 15B pretraining tokens plus chat/instruction tuning
+- **Hardware:** 8x NVIDIA RTX 5090 cloud GPUs
+- **Tokenizer:** TinyLlama/Llama-style 32k tokenizer with a Neo50M chat template
+
+## Intended Uses
+
+- toy/local assistant experiments
+- educational training and inference demos
+- lightweight generation
+- testing HF, GGUF, ONNX, and distributed training pipelines
+
+## Limitations
+
+Neo50M is very small. It is not reliable for factual accuracy, has limited reasoning ability, may hallucinate, and should not be used for safety-critical decisions or high-stakes advice.
+
+## Transformers Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo_id = "KookiesXy/Neo50M"
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForCausalLM.from_pretrained(repo_id, device_map="auto")
+
+messages = [{"role": "user", "content": "Write a short thank-you note."}]
+inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(inputs, max_new_tokens=120, temperature=0.7, top_p=0.9)
+print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+## GGUF Usage
+
+After downloading a GGUF file:
+
+```bash
+llama-cli -m neo50m-q4_k_m.gguf -p "User: Write a haiku about GPUs.\nAssistant:"
+```
+
+## ONNX Usage
+
+The ONNX export is intended for forward-pass validation and integration experiments. Use ONNX Runtime to load `onnx/model.onnx` and feed integer `input_ids` plus `attention_mask`.
+
+## Dataset Summary
+
+The training pipeline streams a configurable mixture of FineWeb-Edu, Cosmopedia, Wikipedia-like text, TinyStories, and a small permissive code component. SFT uses OpenHermes-style, UltraChat-style, Alpaca-style, and small refusal/helpfulness examples when available. Dataset availability can change; the exact configs are included with the upload.
+
+## Eval Results
+
+Eval artifacts, when present, are uploaded under `evals/`.