初始化项目，由ModelHub XC社区提供模型

Model: OPENGCM/Hydrion-v1-Base
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- text-generation
+- causal-lm
+- chatml
+- from-scratch
+- hydrion
+- opengcm
+pipeline_tag: text-generation
+---
+
+
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/69c842686cf758859915159c/KRCENFQ0W7H9B2GruxyA9.png)
+
+Hydrion is a 114M-parameter causal language model, pretrained from scratch and fine-tuned for chat, built on a single RTX 3060 plus a handful of rented A100 hours.
+
+This repo (`OpenGCM/Hydrion-Base`) is the base model, non-chat ready version. The instruction model (chat formatting) is available at [`OpenGCM/Hydrion-SFT`](https://huggingface.co/OPENGCM/Hydrion-SFT).
+## Model Details
+
+- **Architecture:** Llama-style decoder-only transformer (RMSNorm, rotary position embeddings, SwiGLU MLP, grouped-query attention)
+- **Parameters:** 114.1M
+- **Layers:** 12
+- **Hidden size:** 768
+- **Attention heads:** 12 (4 KV heads, GQA)
+- **Context length:** 1024 tokens
+- **Tokenizer:** [`EleutherAI/gpt-neox-20b`](https://huggingface.co/EleutherAI/gpt-neox-20b)
+- **License:** Apache 2.0
+
+## Training
+
+Hydrion was trained in two pretraining stages.
+
+1. **Initial pretraining** — ~2B tokens on a FineWeb-Edu / Wikipedia mix, trained on a single RTX 3060 (12GB).
+2. **Continued pretraining** — an additional ~0.5B tokens on a more diverse mix (FineWeb-Edu, Wikipedia, TinyStories, a code subset, and Dolly), run on a rented A100 to broaden register and topic coverage beyond pure web/encyclopedic text.
+
+Total pretraining exposure: roughly **2.5 billion tokens**.
+
+## Benchmarks
+
+Evaluated with [`lm-evaluation-harness`](https://github.com/EleutherAI/lm-evaluation-harness) on the base (pre-SFT) checkpoint:
+
+| Benchmark | Metric | Score |
+|---|---|---|
+| BLiMP | acc | 80.08% |
+| ARC-Easy | acc | 47.26% |
+| ARC-Easy | acc_norm | 43.39% |
+| WikiText-2 | byte_perplexity | 2.04 |
+| WikiText-2 | bits_per_byte | 1.03 |
+| WikiText-2 | word_perplexity | 45.02 |
+
+Grammatical judgment (BLiMP) is comparable to models trained on far larger token budgets; factual/reasoning performance (ARC-Easy) is meaningfully weaker, consistent with the relatively small pretraining corpus.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, LlamaForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("OPENGCM/Hydrion-Base")
+model = LlamaForCausalLM.from_pretrained("OPENGCM/Hydrion-Base", torch_dtype=torch.bfloat16).cuda()
+model.eval()
+
+prompt = "What is the capital of"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=150,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.9,
+        repetition_penalty=1.3,
+        no_repeat_ngram_size=3,
+        eos_token_id=tokenizer.convert_tokens_to_ids("<|im_end|>"),
+    )
+
+response = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
+## Limitations
+
+Hydrion is a small model trained on a modest token budget (~2.5B tokens, versus the trillions used by comparable production small models). It should **not** be relied on for factual accuracy. It reliably produces fluent, grammatically well-formed English and responds in a conversational chat format, but frequently states incorrect facts, fabricates names/dates/attributions, and performs poorly at arithmetic and multi-step reasoning. Treat outputs as unreliable by default — this model is best understood as a demonstration of a working from-scratch training pipeline rather than a usable knowledge source or assistant.