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

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

3412fb2

1 Parent(s): a266a27

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -60,7 +60,7 @@ We evaluate the model on the datasets of [Open LLM Leaderboard](https://huggingf
 ### Inference Tool
-We utilize [PowerInfer](https://github.com/SJTU-IPADS/PowerInfer) for pure CPU inference, here we list the inference speed of pure CPU inference with fp16 precision.
 Dense Inference: 0.85 tokens/s

 ### Inference Tool
+We utilize [PowerInfer](https://github.com/SJTU-IPADS/PowerInfer) for inference, here we present the inference speeds of pure CPU-based inference with fp16 precision. The CPU configuration includes an Intel i9-13900K processor (eight performance cores at 5.4GHz) and 192GB of host memory (with a memory bandwidth of 67.2 GB/s).
 Dense Inference: 0.85 tokens/s