
![RWKlogo.png](https://s3.amazonaws.com/moonup/production/uploads/62441d1d9fdefb55a0b7d12c/UWpP-lGRZJJDaEx_uUlDv.png)

# Model card for RWKV-4 | 169M 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-4-169m-pile")
tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-169m-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-4-169m-pile").to(0)
tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-169m-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-4-169m-pile", torch_dtype=torch.float16).to(0)
tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-169m-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-4-169m-pile", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-169m-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/)
{"datasets": ["EleutherAI/pile"]}
transformers pytorch rwkv text-generation dataset:EleutherAI/pile autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #rwkv #text-generation #dataset-EleutherAI/pile #autotrain_compatible #endpoints_compatible #has_space #region-us 


!URL

# Model card for RWKV-4 | 169M parameters trained on Pile dataset

RWKV is a project led by Bo Peng. Learn more about the model architecture in the blogposts from Johan Wind here and here. Learn more about the project by joining the RWKV discord server.

# Table of contents

0. TL;DR
1. Model Details
2. Usage
3. Citation

## TL;DR

Below is the description from the original repository
> 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' 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.



### 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>




### Running the model on a single GPU

<details>
<summary> Click to expand </summary>



</details>

</details>

### Running the model in half-precision, on GPU

<details>
<summary> Click to expand </summary>



</details>


### Running the model multiple GPUs
<details>
<summary> Click to expand </summary>



</details>

If you use this model, please consider citing the original work, from the original repo here
# Model card for RWKV-4 | 169M parameters trained on Pile dataset

RWKV is a project led by Bo Peng. Learn more about the model architecture in the blogposts from Johan Wind here and here. Learn more about the project by joining the RWKV discord server. # Table of contents

0. TL;DR
1. Model Details
2. Usage
3. Citation ## TL;DR

Below is the description from the original repository
> 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' 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. ### 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> ### Running the model on a single GPU

<details>
<summary> Click to expand </summary>



</details>

</details> ### Running the model in half-precision, on GPU

<details>
<summary> Click to expand </summary>



</details> ### Running the model multiple GPUs
<details>
<summary> Click to expand </summary>



</details>

If you use this model, please consider citing the original work, from the original repo here
TAGS
#transformers #pytorch #rwkv #text-generation #dataset-EleutherAI/pile #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model card for RWKV-4 | 169M parameters trained on Pile dataset

RWKV is a project led by Bo Peng. Learn more about the model architecture in the blogposts from Johan Wind here and here. Learn more about the project by joining the RWKV discord server. # Table of contents

0. TL;DR
1. Model Details
2. Usage
3. Citation ## TL;DR

Below is the description from the original repository
> 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' 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. ### 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> ### Running the model on a single GPU

<details>
<summary> Click to expand </summary>



</details>

</details> ### Running the model in half-precision, on GPU

<details>
<summary> Click to expand </summary>



</details> ### Running the model multiple GPUs
<details>
<summary> Click to expand </summary>



</details>

If you use this model, please consider citing the original work, from the original repo here