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
```

langflow-lm1b / config.json

nealchen

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	{
	"_name_or_path": "nealchen/langflow-lm1b",
	"architectures": [
	"LangFlow"
	],
	"auto_map": {
	"AutoConfig": "config.LangFlowConfig",
	"AutoModelForMaskedLM": "model.LangFlow"
	},
	"model_type": "LangFlow",
	"vocab_size": 30522,
	"hidden_size": 768,
	"cond_dim": 128,
	"n_blocks": 12,
	"n_heads": 12,
	"dropout": 0.1,
	"model_length": 128,
	"use_normalized_embedding": true,
	"embedding_norm_method": "layernorm",
	"self_conditioning": true,
	"use_bias": true,
	"gumbel_loc": 4.723,
	"gumbel_scale": 0.852,
	"gumbel_cutoff": 1e-5,
	"gumbel_entropy": 7.02,
	"return_dict": true,
	"torch_dtype": "float32",
	"transformers_version": "4.38.2"
	}