Instructions to use Continuous-Rivals-Discrete/langflow-lm1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Continuous-Rivals-Discrete/langflow-lm1b with Transformers:

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

pipe = pipeline("text-generation", model="Continuous-Rivals-Discrete/langflow-lm1b", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("Continuous-Rivals-Discrete/langflow-lm1b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Continuous-Rivals-Discrete/langflow-lm1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Continuous-Rivals-Discrete/langflow-lm1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Continuous-Rivals-Discrete/langflow-lm1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Continuous-Rivals-Discrete/langflow-lm1b

SGLang

How to use Continuous-Rivals-Discrete/langflow-lm1b 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 "Continuous-Rivals-Discrete/langflow-lm1b" \
    --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": "Continuous-Rivals-Discrete/langflow-lm1b",
		"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 "Continuous-Rivals-Discrete/langflow-lm1b" \
        --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": "Continuous-Rivals-Discrete/langflow-lm1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Continuous-Rivals-Discrete/langflow-lm1b with Docker Model Runner:
```
docker model run hf.co/Continuous-Rivals-Discrete/langflow-lm1b
```

nealchen commited on 4 days ago

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@@ -27,7 +27,7 @@ To use the pre-trained model for text generation, use the following snippet:
 from transformers import AutoModelForMaskedLM, AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
-model = AutoModelForMaskedLM.from_pretrained('Continuous-Rivals-Discrete/langflow-owt', trust_remote_code=True)
 # Generate samples
 samples = model.generate_samples(num_samples=5, num_steps=128)

 from transformers import AutoModelForMaskedLM, AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+model = AutoModelForMaskedLM.from_pretrained('Continuous-Rivals-Discrete/langflow-lm1b', trust_remote_code=True)
 # Generate samples
 samples = model.generate_samples(num_samples=5, num_steps=128)