初始化项目，由ModelHub XC社区提供模型

Model: North-ML1/willow-alpha-base
Source: Original Platform

README.md

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+---
+license: mit
+tags:
+- llama
+- pytorch
+- causal-lm
+- base-model
+- north-ml
+- forge
+- willow-alpha
+language:
+- en
+pipeline_tag: text-generation
+---
+
+<h1 align="center" style="font-size: 54px;">
+  Willow Alpha
+</h1>
+
+<p align="center">
+  <b>An early-stage version of Forge-1V</b>
+</p>
+
+<p align="center">
+  <i>Small language model research by North ML.</i>
+</p>
+
+---
+
+## Overview
+
+**Willow Alpha** is an early-stage base model checkpoint in the **Forge-1V** model line.
+
+This model is currently experimental and should be treated as a research checkpoint rather than a polished assistant model. It is useful for testing architecture, pretraining quality, tokenizer behavior, evaluation pipelines, and future SFT/RLHF improvements.
+
+---
+
+## Model Details
+
+| Field | Value |
+|---|---|
+| Model name | Willow Alpha |
+| Project | Forge-1V |
+| Organization | North ML |
+| Model type | Causal Language Model |
+| Language | English |
+| License | MIT |
+| Status | Early-stage / Alpha |
+
+---
+
+## Evaluation Results
+
+All benchmarks below were run in **0-shot** mode.
+
+| Benchmark | Metric | Score | Runtime |
+|---|---:|---:|---:|
+| HellaSwag | acc_norm | 26.71% | 318.67s |
+| PIQA | acc_norm | 53.86% | 38.85s |
+| WinoGrande | acc | 50.67% | 23.73s |
+| BoolQ | acc | 40.21% | 144.80s |
+| ARC-Easy | acc_norm | 34.68% | 51.41s |
+| ARC-Challenge | acc_norm | 25.60% | 37.69s |
+| OpenBookQA | acc_norm | 25.00% | 21.14s |
+| CommonsenseQA | acc | 20.31% | 27.66s |
+| LAMBADA | acc | 0.23% | 96.28s |
+| BLiMP | acc | 59.23% | 354.79s |
+| MMLU | acc | 23.89% | 388.62s |
+| WikiText-2 | word_perplexity | 12524.42 | 182.89s |
+| WikiText-2 | byte_perplexity | 5.84 | 181.42s |
+| SciQ | acc_norm | 35.60% | 87.15s |
+| COPA | acc | 64.00% | 17.21s |
+| RACE | acc | 23.16% | 334.70s |
+| SWAG | acc_norm | 29.13% | 252.00s |
+| TruthfulQA MC2 | acc | 48.74% | 126.29s |
+
+---
+
+## Evaluation Summary
+
+| Category | Result |
+|---|---:|
+| Number of completed benchmark runs | 18 |
+| Successful runs | 18 |
+| Failed runs | 0 |
+| Best accuracy-style score | COPA — 64.00% |
+| Best language-structure score | BLiMP — 59.23% |
+| MMLU score | 23.89% |
+| WikiText-2 byte perplexity | 5.84 |
+| WikiText-2 word perplexity | 12524.42 |
+
+---
+
+## Notes
+
+Willow Alpha is still in a very early stage. Some results are near-random or unstable, especially on knowledge-heavy and long-context tasks.
+
+The strongest early signals are:
+
+- **COPA:** 64.00%
+- **BLiMP:** 59.23%
+- **PIQA:** 53.86%
+- **WinoGrande:** 50.67%
+- **TruthfulQA MC2:** 48.74%
+
+The weakest areas are:
+
+- **LAMBADA**
+- **WikiText-2 word perplexity**
+- **CommonsenseQA**
+- **MMLU**
+- **RACE**
+
+These results suggest the model has some early reasoning and grammar signal, but still needs substantially more pretraining, higher-quality data, and post-training before being useful as a general assistant.
+
+---
+
+## Intended Use
+
+Willow Alpha is intended for:
+
+- Research
+- Benchmarking
+- Pretraining experiments
+- Fine-tuning experiments
+- Small language model development
+- Forge-1V pipeline testing
+
+It is **not yet recommended** for production use.
+
+---
+
+## Limitations
+
+This model may:
+
+- Produce incorrect information
+- Fail basic reasoning tasks
+- Struggle with factual knowledge
+- Generate repetitive or low-quality text
+- Perform poorly on long-context tasks
+- Require additional supervised fine-tuning
+
+---
+
+## Citation
+
+```bibtex
+@misc{willow-alpha,
+  title = {Willow Alpha},
+  author = {North ML},
+  year = {2026},
+  note = {Early-stage Forge-1V checkpoint}
+}