Instructions to use avidoavid/RWKV-1b5-finetuned-overfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use avidoavid/RWKV-1b5-finetuned-overfit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="avidoavid/RWKV-1b5-finetuned-overfit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("avidoavid/RWKV-1b5-finetuned-overfit")
model = AutoModelForCausalLM.from_pretrained("avidoavid/RWKV-1b5-finetuned-overfit", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use avidoavid/RWKV-1b5-finetuned-overfit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "avidoavid/RWKV-1b5-finetuned-overfit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "avidoavid/RWKV-1b5-finetuned-overfit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/avidoavid/RWKV-1b5-finetuned-overfit

SGLang

How to use avidoavid/RWKV-1b5-finetuned-overfit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "avidoavid/RWKV-1b5-finetuned-overfit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "avidoavid/RWKV-1b5-finetuned-overfit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "avidoavid/RWKV-1b5-finetuned-overfit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "avidoavid/RWKV-1b5-finetuned-overfit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use avidoavid/RWKV-1b5-finetuned-overfit with Docker Model Runner:
```
docker model run hf.co/avidoavid/RWKV-1b5-finetuned-overfit
```

avidoavid commited on Jul 27, 2023

Commit

8d26470

1 Parent(s): 32deeaa

update model card README.md

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@@ -5,7 +5,6 @@ tags:
 model-index:
 - name: RWKV-1b5-finetuned-overfit
   results: []
-library_name: peft
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -58,7 +57,6 @@ The following hyperparameters were used during training:
 ### Framework versions
-- PEFT 0.4.0
 - Transformers 4.31.0
 - Pytorch 2.0.1+cu117
 - Datasets 2.13.1

 model-index:
 - name: RWKV-1b5-finetuned-overfit
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 ### Framework versions
 - Transformers 4.31.0
 - Pytorch 2.0.1+cu117
 - Datasets 2.13.1