Instructions to use OpenMOSE/HRWKV7-Reka-Flash3-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenMOSE/HRWKV7-Reka-Flash3-Preview with Transformers:

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

pipe = pipeline("text-generation", model="OpenMOSE/HRWKV7-Reka-Flash3-Preview")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OpenMOSE/HRWKV7-Reka-Flash3-Preview", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenMOSE/HRWKV7-Reka-Flash3-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenMOSE/HRWKV7-Reka-Flash3-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenMOSE/HRWKV7-Reka-Flash3-Preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenMOSE/HRWKV7-Reka-Flash3-Preview

SGLang

How to use OpenMOSE/HRWKV7-Reka-Flash3-Preview 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 "OpenMOSE/HRWKV7-Reka-Flash3-Preview" \
    --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": "OpenMOSE/HRWKV7-Reka-Flash3-Preview",
		"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 "OpenMOSE/HRWKV7-Reka-Flash3-Preview" \
        --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": "OpenMOSE/HRWKV7-Reka-Flash3-Preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenMOSE/HRWKV7-Reka-Flash3-Preview with Docker Model Runner:
```
docker model run hf.co/OpenMOSE/HRWKV7-Reka-Flash3-Preview
```

OpenMOSE commited on Jul 5, 2025

Commit

1a4439c

verified ·

1 Parent(s): 157ff0e

Update README.md

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README.md +8 -0

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@@ -69,6 +69,14 @@ Performance evaluation is ongoing. The model shows promising results in:
 - Significantly improved needle-in-haystack task performance compared to pure RWKV architectures
 - Competitive performance on standard language modeling benchmarks
 ## Thank you for Big help :)
 - SmerkyG Inspired by RADLADS (https://arxiv.org/abs/2505.03005)

 - Significantly improved needle-in-haystack task performance compared to pure RWKV architectures
 - Competitive performance on standard language modeling benchmarks
+## Run
+- RWKV-Infer now support hxa079
+```bash
+curl http://127.0.0.1:9000/loadmodel -X POST -H "Content-Type: application/json" -d '{"model_filename":"/home/client/Projects/llm/hxa079-reka-flash3-stage2-hybrid.pth","model_viewname":"RWKV HXA079 L38T6 Reka Flash3","model_strategy":"int8","adapter_filename":"","adapter_mode":"", "template":"rekaflash3", "endtoken":"\n <sep>","default_temperature":"1.0", "default_top_p":"0.3", "rope_theta":"8000000.0", "rms_norm_eps":"1e-5"}'
+```
 ## Thank you for Big help :)
 - SmerkyG Inspired by RADLADS (https://arxiv.org/abs/2505.03005)