
# RWKV-4 1.5B

# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.

## Model Description

RWKV-4 1.5B is a L24-D2048 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 = 24
n_embd = 2048

RWKV-4-Pile-1B5-20220929-ctx4096.pth : Fine-tuned to ctx_len 4096.
* Likely better when ctxlen > 100. Please test.

RWKV-4-Pile-1B5-20220903-8040.pth : Trained on the Pile for 332B tokens.
* Pile loss 2.0415
* LAMBADA ppl 7.04, acc 56.43%
* PIQA acc 72.36%
* SC2016 acc 68.73%
* Hellaswag acc_norm 52.48%

### Instruct-test models: 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-1B5-Instruct-test1-20230124.pth
instruct-tuned on https://huggingface.co/datasets/bigscience/xP3all/viewer/en/train

RWKV-4-Pile-1B5-Instruct-test2-20230209.pth
instruct-tuned on https://huggingface.co/datasets/Muennighoff/flan & NIv2

### Chinese models

RWKV-4-Pile-1B5-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.)

RWKV-4-Pile-1B5-EngChn-testxxx for Chinese Q&A (trained on 10G Chinese text. only for testing purposes.)

## Note: 4 / 4a / 4b models ARE NOT compatible. Use RWKV-4 unless you know what you are doing.

RWKV-4b-Pile-1B5-20230217-7954.pth (--my_testing 'a') with tiny amt of QKV attention to improve performance
* Pile loss 1.9947
* LAMBADA ppl 5.82, acc 62.35%
* PIQA acc 72.52%
* SC2016 acc 68.89%
* Hellaswag acc_norm 54.32%

{"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 1.5B

# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.
# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.

## Model Description

RWKV-4 1.5B is a L24-D2048 causal language model trained on the Pile. See URL for details.

Use URL to run it.

ctx_len = 1024
n_layer = 24
n_embd = 2048

URL : Fine-tuned to ctx_len 4096.
* Likely better when ctxlen > 100. Please test.

URL : Trained on the Pile for 332B tokens.
* Pile loss 2.0415
* LAMBADA ppl 7.04, acc 56.43%
* PIQA acc 72.36%
* SC2016 acc 68.73%
* Hellaswag acc_norm 52.48%

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

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

URL
instruct-tuned on URL

URL
instruct-tuned on URL & NIv2

### Chinese models

RWKV-4-Pile-1B5-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.)

RWKV-4-Pile-1B5-EngChn-testxxx for Chinese Q&A (trained on 10G Chinese text. only for testing purposes.)

## Note: 4 / 4a / 4b models ARE NOT compatible. Use RWKV-4 unless you know what you are doing.

URL (--my_testing 'a') with tiny amt of QKV attention to improve performance
* Pile loss 1.9947
* LAMBADA ppl 5.82, acc 62.35%
* PIQA acc 72.52%
* SC2016 acc 68.89%
* Hellaswag acc_norm 54.32%

# RWKV-4 1.5B # Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing. # Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing. # Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing. ## Model Description

RWKV-4 1.5B is a L24-D2048 causal language model trained on the Pile. See URL for details.

Use URL to run it.

ctx_len = 1024
n_layer = 24
n_embd = 2048

URL : Fine-tuned to ctx_len 4096.
* Likely better when ctxlen > 100. Please test.

URL : Trained on the Pile for 332B tokens.
* Pile loss 2.0415
* LAMBADA ppl 7.04, acc 56.43%
* PIQA acc 72.36%
* SC2016 acc 68.73%
* Hellaswag acc_norm 52.48% ### Instruct-test models: only useful if you construct your prompt following dataset templates

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

URL
instruct-tuned on URL

URL
instruct-tuned on URL & NIv2 ### Chinese models

RWKV-4-Pile-1B5-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.)

RWKV-4-Pile-1B5-EngChn-testxxx for Chinese Q&A (trained on 10G Chinese text. only for testing purposes.) ## Note: 4 / 4a / 4b models ARE NOT compatible. Use RWKV-4 unless you know what you are doing.

URL (--my_testing 'a') with tiny amt of QKV attention to improve performance
* Pile loss 1.9947
* LAMBADA ppl 5.82, acc 62.35%
* PIQA acc 72.52%
* SC2016 acc 68.89%
* Hellaswag acc_norm 54.32%
TAGS
#pytorch #text-generation #causal-lm #rwkv #en #dataset-the_pile #license-apache-2.0 #has_space #region-us 
 # RWKV-4 1.5B # Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing. # Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing. # Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing. ## Model Description

RWKV-4 1.5B is a L24-D2048 causal language model trained on the Pile. See URL for details.

Use URL to run it.

ctx_len = 1024
n_layer = 24
n_embd = 2048

URL : Fine-tuned to ctx_len 4096.
* Likely better when ctxlen > 100. Please test.

URL : Trained on the Pile for 332B tokens.
* Pile loss 2.0415
* LAMBADA ppl 7.04, acc 56.43%
* PIQA acc 72.36%
* SC2016 acc 68.73%
* Hellaswag acc_norm 52.48% ### Instruct-test models: only useful if you construct your prompt following dataset templates

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

URL
instruct-tuned on URL

URL
instruct-tuned on URL & NIv2 ### Chinese models

RWKV-4-Pile-1B5-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.)

RWKV-4-Pile-1B5-EngChn-testxxx for Chinese Q&A (trained on 10G Chinese text. only for testing purposes.) ## Note: 4 / 4a / 4b models ARE NOT compatible. Use RWKV-4 unless you know what you are doing.

URL (--my_testing 'a') with tiny amt of QKV attention to improve performance
* Pile loss 1.9947
* LAMBADA ppl 5.82, acc 62.35%
* PIQA acc 72.52%
* SC2016 acc 68.89%
* Hellaswag acc_norm 54.32%
passage: TAGS
#pytorch #text-generation #causal-lm #rwkv #en #dataset-the_pile #license-apache-2.0 #has_space #region-us 
# RWKV-4 1.5B# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.# Use RWKV-4 models (NOT RWKV-4a, NOT RWKV-4b) unless you know what you are doing.## Model Description

RWKV-4 1.5B is a L24-D2048 causal language model trained on the Pile. See URL for details.

Use URL to run it.

ctx_len = 1024
n_layer = 24
n_embd = 2048

URL : Fine-tuned to ctx_len 4096.
* Likely better when ctxlen > 100. Please test.

URL : Trained on the Pile for 332B tokens.
* Pile loss 2.0415
* LAMBADA ppl 7.04, acc 56.43%
* PIQA acc 72.36%
* SC2016 acc 68.73%
* Hellaswag acc_norm 52.48%### Instruct-test models: only useful if you construct your prompt following dataset templates

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

URL
instruct-tuned on URL

URL
instruct-tuned on URL & NIv2### Chinese models

RWKV-4-Pile-1B5-EngChn-testNovel-xxx for writing Chinese novels (trained on 200G Chinese novels.)

RWKV-4-Pile-1B5-EngChn-testxxx for Chinese Q&A (trained on 10G Chinese text. only for testing purposes.)