Instructions to use TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF with Transformers:

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

pipe = pipeline("text-generation", model="TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF

SGLang

How to use TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF 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 "TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF" \
    --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": "TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF",
		"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 "TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF" \
        --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": "TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF with Docker Model Runner:
```
docker model run hf.co/TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF
```

TimeMobius commited on Mar 27, 2024

Commit

1964fc8

verified ·

1 Parent(s): 1d66d00

Update README.md

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@@ -17,9 +17,9 @@ Response:"""
         return f"""User: {instruction}
 Assistant:"""
-#model = AutoModelForCausalLM.from_pretrained("TimeMobius/Mobius-Chat-12B-128k-HF", trust_remote_code=True, torch_dtype=torch.bfloat16).to(0)
-model = AutoModelForCausalLM.from_pretrained("TimeMobius/Mobius-Chat-12B-128k-HF", trust_remote_code=True, torch_dtype=torch.float16).to(0)
-tokenizer = AutoTokenizer.from_pretrained("TimeMobius/Mobius-Chat-12B-128k-HF", trust_remote_code=True)
 text = "Write a beginning of sci-fi novel"
 prompt = generate_prompt(text)
 inputs = tokenizer(prompt, return_tensors="pt").to(0)

         return f"""User: {instruction}
 Assistant:"""
+#model = AutoModelForCausalLM.from_pretrained("TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF", trust_remote_code=True, torch_dtype=torch.bfloat16).to(0)
+model = AutoModelForCausalLM.from_pretrained("TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF", trust_remote_code=True, torch_dtype=torch.float16).to(0)
+tokenizer = AutoTokenizer.from_pretrained("TimeMobius/Mobius-RWKV-Chat-12B-128k-v4-HF", trust_remote_code=True)
 text = "Write a beginning of sci-fi novel"
 prompt = generate_prompt(text)
 inputs = tokenizer(prompt, return_tensors="pt").to(0)