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datasets: |
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- EleutherAI/pile |
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# Model card for RWKV-4 | 169M parameters trained on Pile dataset |
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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 / https://johanwind.github.io/2023/03/23/rwkv_details.html) and [here](https://johanwind.github.io/2023/03/23/rwkv_overview.html / 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). |
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# Table of contents |
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0. [TL;DR](#TL;DR) |
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1. [Model Details](#model-details) |
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2. [Usage](#usage) |
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3. [Citation](#citation) |
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## TL;DR |
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Below is the description from the original repository |
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> 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. |
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## Model Details |
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The details of the architecture can be found on the blogpost mentioned above and the Hugging Face blogpost of the integration. |
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## Usage |
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### Convert the raw weights to the HF format |
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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. |
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```bash |
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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 |
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``` |
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### Generate text |
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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: |
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### Running the model on a CPU |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-4-169m-pile") |
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tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-169m-pile") |
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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." |
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inputs = tokenizer(prompt, return_tensors="pt") |
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output = model.generate(inputs["input_ids"], max_new_tokens=40) |
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print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True)) |
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``` |
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### Running the model on a single GPU |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-4-169m-pile").to(0) |
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tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-169m-pile") |
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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." |
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inputs = tokenizer(prompt, return_tensors="pt").to(0) |
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output = model.generate(inputs["input_ids"], max_new_tokens=40) |
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print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True)) |
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``` |
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</details> |
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</details> |
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### Running the model in half-precision, on GPU |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-4-169m-pile", torch_dtype=torch.float16).to(0) |
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tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-169m-pile") |
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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." |
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inputs = tokenizer(prompt, return_tensors="pt").to(0) |
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output = model.generate(inputs["input_ids"], max_new_tokens=40) |
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print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True)) |
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``` |
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</details> |
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### Running the model multiple GPUs |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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# pip install accelerate |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-4-169m-pile", device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-169m-pile") |
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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." |
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inputs = tokenizer(prompt, return_tensors="pt").to(0) |
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output = model.generate(inputs["input_ids"], max_new_tokens=40) |
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print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True)) |
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``` |
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</details> |
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## Citation |
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If you use this model, please consider citing the original work, from the original repo [here](https://github.com/BlinkDL/ChatRWKV/) |
