
# RWKV-4 7B

[UPDATE: Try RWKV-4-World (https://huggingface.co/BlinkDL/rwkv-4-world) for generation & chat & code in 100+ world languages, with great English zero-shot & in-context learning ability too.]

## Model Description

RWKV-4 7B is a L32-D4096 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details.

Use https://github.com/BlinkDL/ChatRWKV to run it.

ctx_len = 1024
n_layer = 32
n_embd = 4096

RWKV-4-Pile-7B-20230109-ctx4096.pth : Fine-tuned to ctx_len 4096.
* Likely the best. Please test.

################################

"Raven": RWKV alpaca+vicuna-style model: https://huggingface.co/BlinkDL/rwkv-4-raven (highly recommended)

It is a strong chat model too. You can use +i for "Alpaca Instruct" in latest ChatRWKV v2. Examples:
```
+i Explain the following metaphor: "Life is like cats". 
+i write a python function to read data from an excel file.
```
################################

RWKV-4-Pile-7B-20230xxx-ctx8192-testxxx : Fine-tuned to ctx_len 8192.
* Slightly weaker than ctx4096 model when ctxlen < 3k.

RWKV-4-Pile-7B-20221115-8047.pth : Trained on the Pile for 332B tokens.
* Pile loss 1.8415T
* LAMBADA ppl 4.38, acc 67.18%
* PIQA acc 76.06%
* SC2016 acc 73.44%
* Hellaswag acc_norm 65.51%

### Instruct-test models (OLD): only useful if you construct your prompt following dataset templates

Note I am using "Q: instruct\n\nA: result" prompt for all instructs.

RWKV-4-Pile-7B-Instruct-test1
instruct-tuned on https://huggingface.co/datasets/bigscience/xP3all/viewer/en/train

RWKV-4-Pile-7B-Instruct-test2
instruct-tuned on https://huggingface.co/datasets/Muennighoff/flan & NIv2

### Chinese models

RWKV-4-Pile-7B-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.)

{"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "text-generation", "causal-lm", "rwkv"], "datasets": ["the_pile"]}
pytorch text-generation causal-lm rwkv en dataset:the_pile license:apache-2.0 has_space region:us
TAGS
#pytorch #text-generation #causal-lm #rwkv #en #dataset-the_pile #license-apache-2.0 #has_space #region-us 


# RWKV-4 7B

[UPDATE: Try RWKV-4-World (URL for generation & chat & code in 100+ world languages, with great English zero-shot & in-context learning ability too.]

## Model Description

RWKV-4 7B is a L32-D4096 causal language model trained on the Pile. See URL for details.

Use URL to run it.

ctx_len = 1024
n_layer = 32
n_embd = 4096

URL : Fine-tuned to ctx_len 4096.
* Likely the best. Please test.

################################

"Raven": RWKV alpaca+vicuna-style model: URL (highly recommended)

It is a strong chat model too. You can use +i for "Alpaca Instruct" in latest ChatRWKV v2. Examples:

################################

RWKV-4-Pile-7B-20230xxx-ctx8192-testxxx : Fine-tuned to ctx_len 8192.
* Slightly weaker than ctx4096 model when ctxlen < 3k.

URL : Trained on the Pile for 332B tokens.
* Pile loss 1.8415T
* LAMBADA ppl 4.38, acc 67.18%
* PIQA acc 76.06%
* SC2016 acc 73.44%
* Hellaswag acc_norm 65.51%

### Instruct-test models (OLD): only useful if you construct your prompt following dataset templates

Note I am using "Q: instruct\n\nA: result" prompt for all instructs.

RWKV-4-Pile-7B-Instruct-test1
instruct-tuned on URL

RWKV-4-Pile-7B-Instruct-test2
instruct-tuned on URL & NIv2

### Chinese models

RWKV-4-Pile-7B-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.)

# RWKV-4 7B

[UPDATE: Try RWKV-4-World (URL for generation & chat & code in 100+ world languages, with great English zero-shot & in-context learning ability too.] ## Model Description

RWKV-4 7B is a L32-D4096 causal language model trained on the Pile. See URL for details.

Use URL to run it.

ctx_len = 1024
n_layer = 32
n_embd = 4096

URL : Fine-tuned to ctx_len 4096.
* Likely the best. Please test. ### Instruct-test models (OLD): only useful if you construct your prompt following dataset templates

Note I am using "Q: instruct\n\nA: result" prompt for all instructs.

RWKV-4-Pile-7B-Instruct-test1
instruct-tuned on URL

RWKV-4-Pile-7B-Instruct-test2
instruct-tuned on URL & NIv2 ### Chinese models

RWKV-4-Pile-7B-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.)
TAGS
#pytorch #text-generation #causal-lm #rwkv #en #dataset-the_pile #license-apache-2.0 #has_space #region-us 
 # RWKV-4 7B

[UPDATE: Try RWKV-4-World (URL for generation & chat & code in 100+ world languages, with great English zero-shot & in-context learning ability too.] ## Model Description

RWKV-4 7B is a L32-D4096 causal language model trained on the Pile. See URL for details.

Use URL to run it.

ctx_len = 1024
n_layer = 32
n_embd = 4096

URL : Fine-tuned to ctx_len 4096.
* Likely the best. Please test. ### Instruct-test models (OLD): only useful if you construct your prompt following dataset templates

Note I am using "Q: instruct\n\nA: result" prompt for all instructs.

RWKV-4-Pile-7B-Instruct-test1
instruct-tuned on URL

RWKV-4-Pile-7B-Instruct-test2
instruct-tuned on URL & NIv2 ### Chinese models

RWKV-4-Pile-7B-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.)
passage: TAGS
#pytorch #text-generation #causal-lm #rwkv #en #dataset-the_pile #license-apache-2.0 #has_space #region-us 
# RWKV-4 7B

[UPDATE: Try RWKV-4-World (URL for generation & chat & code in 100+ world languages, with great English zero-shot & in-context learning ability too.]## Model Description

RWKV-4 7B is a L32-D4096 causal language model trained on the Pile. See URL for details.

Use URL to run it.

ctx_len = 1024
n_layer = 32
n_embd = 4096

URL : Fine-tuned to ctx_len 4096.
* Likely the best. Please test.### Instruct-test models (OLD): only useful if you construct your prompt following dataset templates

Note I am using "Q: instruct\n\nA: result" prompt for all instructs.

RWKV-4-Pile-7B-Instruct-test1
instruct-tuned on URL

RWKV-4-Pile-7B-Instruct-test2
instruct-tuned on URL & NIv2### Chinese models

RWKV-4-Pile-7B-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.)