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Model: Ethangou/attention-residuals-100M-baseline
Source: Original Platform

README.md

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+---
+license: mit
+language:
+- zh
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+- opencsg/Fineweb-Edu-Chinese-V2.2
+metrics:
+- perplexity
+- accuracy
+tags:
+- pytorch
+- transformers
+- causal-lm
+- qwen3
+- chinese
+- from-scratch
+- attention-residuals
+- baseline
+- standard-residual
+---
+
+# Attention Residuals 100M Baseline
+
+This is the 100M baseline checkpoint for the attention-residuals-reproduction project.
+It uses a standard Qwen3-style decoder-only Transformer with standard residual connections, trained from scratch on Chinese data.
+
+## Model Details
+
+- Mode: `baseline`
+- Architecture: Qwen3-style causal language model
+- Residual type: standard residual connection
+- Hidden size: 512
+- Layers: 12
+- Attention heads: 8
+- KV heads: 4
+- FFN intermediate size: 1536
+- Sequence length: 2048
+- Training steps: 20,000
+- Training data: `opencsg/Fineweb-Edu-Chinese-V2.2`
+
+## Intended Use
+
+This checkpoint is mainly intended for research comparison with Attention Residuals variants.
+It is not instruction-tuned and should not be used as a chat model.
+
+## Evaluation
+
+| Metric | Result |
+|--------|--------|
+| Chinese Held-out PPL | 128.58 |
+| C-Eval Acc | 0.2664 |
+| CMMLU Acc | 0.2594 |
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo_id = "你的用户名/attention-residuals-100M-baseline"
+
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForCausalLM.from_pretrained(
+    repo_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+prompt = "人工智能的发展"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.8,
+    top_p=0.95,
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))