Update README.md

README.md

@@ -1,3 +1,92 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+- zh
+- ja
+- ko
+- fr
+- ar
+- es
+- pt
+metrics:
+- accuracy
+base_model:
+- BlinkDL/rwkv-7-world
+pipeline_tag: text-generation
+---
+
+# rwkv7-1.5B-world 
+
+<!-- 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:** RWKV7
+- **Language(s) (NLP):** English
+- **License:** Apache-2.0
+- **Parameter count:** 1.52B
+- **Tokenizer:** RWKV World tokenizer
+- **Vocabulary size:** 65,536
+
+### Model Sources
+
+<!-- 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
+
+## 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` and the latest version of `transformers` before using this model:
+
+```bash
+pip install git+https://github.com/fla-org/flash-linear-attention
+pip install 'transformers>=4.48.0'
+```
+
+### 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:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained('fla-hub/rwkv7-1.5B-world', trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained('fla-hub/rwkv7-1.5B-world', trust_remote_code=True)
+```
+
+## Training Details
+
+### Training Data
+
+This model is trained on the World v2.8 with a total of 1.0 trillion tokens.
+
+#### Training Hyperparameters
+
+- **Training regime:** bfloat16, lr 4e-4 to 1e-5 "delayed" cosine decay, wd 0.1 (with increasing batch sizes during the middle)
+- **Final Loss:** 1.9965
+
+## Evaluation
+
+#### Metrics
+
+`lambada_openai`: 
+
+before conversion: ppl 4.13 acc 69.4% 
+
+after conversion: ppl 4.26 acc 68.8%
+
+## FAQ
+Q: safetensors metadata is none.
+
+A: upgrade transformers to >=4.48.0: `pip install 'transformers>=4.48.0'`