Instructions to use epinnock/wizardcoder-1b-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use epinnock/wizardcoder-1b-merged with Transformers:

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

pipe = pipeline("text-generation", model="epinnock/wizardcoder-1b-merged")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("epinnock/wizardcoder-1b-merged")
model = AutoModelForCausalLM.from_pretrained("epinnock/wizardcoder-1b-merged")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use epinnock/wizardcoder-1b-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "epinnock/wizardcoder-1b-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "epinnock/wizardcoder-1b-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/epinnock/wizardcoder-1b-merged

SGLang

How to use epinnock/wizardcoder-1b-merged 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 "epinnock/wizardcoder-1b-merged" \
    --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": "epinnock/wizardcoder-1b-merged",
		"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 "epinnock/wizardcoder-1b-merged" \
        --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": "epinnock/wizardcoder-1b-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use epinnock/wizardcoder-1b-merged with Docker Model Runner:
```
docker model run hf.co/epinnock/wizardcoder-1b-merged
```

epinnock

pedrovhb commited on Sep 4, 2023

Commit

59a8a70

1 Parent(s): ba94ecb

Update readme to fix typo in model ID (#3)

Browse files

- Update readme to fix typo in model ID (b5fb0f9d14658b01881385565d481189814b99bb)

Co-authored-by: Pedro Batista <pedrovhb@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -37,7 +37,7 @@ Based on Previous works of @WizardLM
 # pip install -q transformers
 from transformers import AutoModelForCausalLM, AutoTokenizer
-checkpoint = "epinnock/wizardcoder1b-merged"
 device = "cuda" # for GPU usage or "cpu" for CPU usage
 tokenizer = AutoTokenizer.from_pretrained(checkpoint)

 # pip install -q transformers
 from transformers import AutoModelForCausalLM, AutoTokenizer
+checkpoint = "epinnock/wizardcoder-1b-merged"
 device = "cuda" # for GPU usage or "cpu" for CPU usage
 tokenizer = AutoTokenizer.from_pretrained(checkpoint)