Instructions to use SebastianSchramm/Cerebras-GPT-111M-instruction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SebastianSchramm/Cerebras-GPT-111M-instruction with Transformers:

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

pipe = pipeline("text-generation", model="SebastianSchramm/Cerebras-GPT-111M-instruction")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SebastianSchramm/Cerebras-GPT-111M-instruction")
model = AutoModelForCausalLM.from_pretrained("SebastianSchramm/Cerebras-GPT-111M-instruction", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SebastianSchramm/Cerebras-GPT-111M-instruction with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SebastianSchramm/Cerebras-GPT-111M-instruction"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SebastianSchramm/Cerebras-GPT-111M-instruction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SebastianSchramm/Cerebras-GPT-111M-instruction

SGLang

How to use SebastianSchramm/Cerebras-GPT-111M-instruction 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 "SebastianSchramm/Cerebras-GPT-111M-instruction" \
    --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": "SebastianSchramm/Cerebras-GPT-111M-instruction",
		"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 "SebastianSchramm/Cerebras-GPT-111M-instruction" \
        --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": "SebastianSchramm/Cerebras-GPT-111M-instruction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SebastianSchramm/Cerebras-GPT-111M-instruction with Docker Model Runner:
```
docker model run hf.co/SebastianSchramm/Cerebras-GPT-111M-instruction
```

SebastianSchramm commited on Nov 28, 2023

Commit

334dea5

1 Parent(s): 1436923

adding basemodel link

Browse files

Files changed (1) hide show

README.md +1 -0

README.md CHANGED Viewed

@@ -7,6 +7,7 @@ tags:
 - cerebras
 - LLM
 inference: false
 ---
 # Instruction-tuned Cerebras GPT 111M

 - cerebras
 - LLM
 inference: false
+base_model: cerebras/Cerebras-GPT-111M
 ---
 # Instruction-tuned Cerebras GPT 111M