Instructions to use SparseLLM/ReluFalcon-40B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SparseLLM/ReluFalcon-40B with Transformers:

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

pipe = pipeline("text-generation", model="SparseLLM/ReluFalcon-40B", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SparseLLM/ReluFalcon-40B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("SparseLLM/ReluFalcon-40B", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SparseLLM/ReluFalcon-40B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SparseLLM/ReluFalcon-40B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SparseLLM/ReluFalcon-40B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SparseLLM/ReluFalcon-40B

SGLang

How to use SparseLLM/ReluFalcon-40B 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 "SparseLLM/ReluFalcon-40B" \
    --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": "SparseLLM/ReluFalcon-40B",
		"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 "SparseLLM/ReluFalcon-40B" \
        --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": "SparseLLM/ReluFalcon-40B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SparseLLM/ReluFalcon-40B with Docker Model Runner:
```
docker model run hf.co/SparseLLM/ReluFalcon-40B
```

Yixin Song commited on Dec 15, 2023

Commit

0ee1606

1 Parent(s): 8c1cd98

Update modeling_falcon.py

Browse files

Files changed (1) hide show

modeling_falcon.py +0 -1

modeling_falcon.py CHANGED Viewed

@@ -401,7 +401,6 @@ class FalconMLP(nn.Module):
         hidden_size = config.hidden_size
         self.dense_h_to_4h = FalconLinear(hidden_size, 4 * hidden_size, bias=config.bias)
-        # self.act = nn.GELU()
         self.act = nn.ReLU()
         self.dense_4h_to_h = FalconLinear(4 * hidden_size, hidden_size, bias=config.bias)
         self.hidden_dropout = config.hidden_dropout

         hidden_size = config.hidden_size
         self.dense_h_to_4h = FalconLinear(hidden_size, 4 * hidden_size, bias=config.bias)
         self.act = nn.ReLU()
         self.dense_4h_to_h = FalconLinear(4 * hidden_size, hidden_size, bias=config.bias)
         self.hidden_dropout = config.hidden_dropout