Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# rwkv7-168m-pile
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This is RWKV-7 model under flash-linear attention format.
+
+## Model Details
+
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** Bo Peng, Yu Zhang, Songlin Yang, Ruochong Zhang
+- **Funded by:** Shenzhen Yuanshi Intelligent Co. Ltd.
+- **Model type:** RWKV-7
+- **Language(s) (NLP):** English
+- **License:** Apache-2.0
+- **Parameter count:** 165M
+- **Tokenizer:** GPT-NeoX 20B tokenizer
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/fla-org/flash-linear-attention ; https://github.com/BlinkDL/RWKV-LM
+- **Paper:** With in Progress
+- **Weights:** Converted from https://modelscope.cn/models/RWKV/rwkv-7-pile/file/view/master?fileName=RWKV-x070-Pile-168M-20241120-ctx4096.pth
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Install flash-linear-attention before using this model:
+
+```
+git clone https://github.com/fla-org/flash-linear-attention
+cd flash-linear-attention
+pip install -e .
+```
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+You can use this model just as any other HuggingFace models:
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained('fla-hub/rwkv7-168m-pile', trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained('fla-hub/rwkv7-168m-pile', trust_remote_code=True)
+```
+
+## Training Details
+
+### Training Data
+
+This model is trained on the Pile with a total of 332 billion tokens.
+
+#### Training Hyperparameters
+
+- **Training regime:** bfloat16, lr 8e-4 to 3e-5 cosine decay, wd 0.1, bsz 8x30x4096
+
+## Evaluation
+
+#### Metrics
+
+`lambada_openai`: ppl 14.2 acc 45.6%
+`piqa`: acc 65.5%