Create README.md

README.md

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+---
+datasets:
+- EleutherAI/pile
+---
+
+![RWKlogo.png](https://s3.amazonaws.com/moonup/production/uploads/62441d1d9fdefb55a0b7d12c/UWpP-lGRZJJDaEx_uUlDv.png)
+
+# Model card for RWKV-4 | 3B parameters trained on Pile dataset
+
+RWKV is a project led by [Bo Peng](https://github.com/BlinkDL). Learn more about the model architecture in the blogposts from Johan Wind [here](https://johanwind.github.io/2023/03/23/rwkv_overview.html) and [here](https://johanwind.github.io/2023/03/23/rwkv_details.html). Learn more about the project by joining the [RWKV discord server](https://discordapp.com/users/468093332535640064).
+
+# Table of contents
+
+0. [TL;DR](#TL;DR)
+1. [Model Details](#model-details)
+2. [Usage](#usage)
+3. [Citation](#citation)
+
+## TL;DR
+
+Below is the description from the [original repository](https://github.com/BlinkDL/RWKV-LM)
+> RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). It's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
+
+## Model Details
+
+The details of the architecture can be found on the blogpost mentioned above and the Hugging Face blogpost of the integration.
+
+## Usage
+
+### Convert the raw weights to the HF format
+
+You can use the [`convert_rwkv_checkpoint_to_hf.py`](https://github.com/huggingface/transformers/tree/main/src/transformers/models/rwkv/convert_rwkv_checkpoint_to_hf.py) script by specifying the repo_id of the original weights, the filename and the output directory. You can also optionally directly push the converted model on the Hub by passing `--push_to_hub` flag and `--model_name` argument to specify where to push the converted weights.
+
+```bash
+python convert_rwkv_checkpoint_to_hf.py --repo_id RAW_HUB_REPO --checkpoint_file RAW_FILE --output_dir OUTPUT_DIR --push_to_hub --model_name dummy_user/converted-rwkv
+```
+
+### Generate text
+
+You can use the `AutoModelForCausalLM` and `AutoTokenizer` classes to generate texts from the model. Expand the sections below to understand how to run the model in different scenarios:
+
+### Running the model on a CPU
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-4-3b-pile")
+tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-3b-pile")
+
+prompt = "\nIn a shocking finding, scientist discovered a herd of dragons living in a remote, previously unexplored valley, in Tibet. Even more surprising to the researchers was the fact that the dragons spoke perfect Chinese."
+
+inputs = tokenizer(prompt, return_tensors="pt")
+output = model.generate(inputs["input_ids"], max_new_tokens=40)
+print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
+```
+
+
+### Running the model on a single GPU
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-4-3b-pile").to(0)
+tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-3b-pile")
+
+prompt = "\nIn a shocking finding, scientist discovered a herd of dragons living in a remote, previously unexplored valley, in Tibet. Even more surprising to the researchers was the fact that the dragons spoke perfect Chinese."
+
+inputs = tokenizer(prompt, return_tensors="pt").to(0)
+output = model.generate(inputs["input_ids"], max_new_tokens=40)
+print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
+```
+
+</details>
+
+</details>
+
+### Running the model in half-precision, on GPU
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-4-3b-pile", torch_dtype=torch.float16).to(0)
+tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-3b-pile")
+
+prompt = "\nIn a shocking finding, scientist discovered a herd of dragons living in a remote, previously unexplored valley, in Tibet. Even more surprising to the researchers was the fact that the dragons spoke perfect Chinese."
+
+inputs = tokenizer(prompt, return_tensors="pt").to(0)
+output = model.generate(inputs["input_ids"], max_new_tokens=40)
+print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
+```
+
+</details>
+
+
+### Running the model multiple GPUs
+<details>
+<summary> Click to expand </summary>
+
+```python
+# pip install accelerate
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-4-3b-pile", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-3b-pile")
+
+prompt = "\nIn a shocking finding, scientist discovered a herd of dragons living in a remote, previously unexplored valley, in Tibet. Even more surprising to the researchers was the fact that the dragons spoke perfect Chinese."
+
+inputs = tokenizer(prompt, return_tensors="pt").to(0)
+output = model.generate(inputs["input_ids"], max_new_tokens=40)
+print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
+```
+
+</details>
+
+## Citation
+
+If you use this model, please consider citing the original work, from the original repo [here](https://github.com/BlinkDL/ChatRWKV/)