Instructions to use RedHatAI/starcoder2-3b-quantized.w8a16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/starcoder2-3b-quantized.w8a16 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/starcoder2-3b-quantized.w8a16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/starcoder2-3b-quantized.w8a16")
model = AutoModelForMultimodalLM.from_pretrained("RedHatAI/starcoder2-3b-quantized.w8a16")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RedHatAI/starcoder2-3b-quantized.w8a16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/starcoder2-3b-quantized.w8a16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/starcoder2-3b-quantized.w8a16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RedHatAI/starcoder2-3b-quantized.w8a16

SGLang

How to use RedHatAI/starcoder2-3b-quantized.w8a16 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 "RedHatAI/starcoder2-3b-quantized.w8a16" \
    --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": "RedHatAI/starcoder2-3b-quantized.w8a16",
		"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 "RedHatAI/starcoder2-3b-quantized.w8a16" \
        --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": "RedHatAI/starcoder2-3b-quantized.w8a16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RedHatAI/starcoder2-3b-quantized.w8a16 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/starcoder2-3b-quantized.w8a16
```

alexmarques commited on Aug 2, 2024

Commit

3e2943b

verified ·

1 Parent(s): b2915c2

Create README.md

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+---
+pipeline_tag: text-generation
+inference: true
+widget:
+- text: 'def print_hello_world():'
+  example_title: Hello world
+  group: Python
+datasets:
+- bigcode/the-stack-v2-train
+license: bigcode-openrail-m
+library_name: transformers
+tags:
+- code
+model-index:
+- name: starcoder2-3b-quantized.w8a16
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      name: HumanEval+
+      type: humanevalplus
+    metrics:
+    - type: pass@1
+      value: 26.4
+  - task:
+      type: text-generation
+    dataset:
+      name: HumanEval
+      type: humaneval
+    metrics:
+    - type: pass@1
+      value: 31.0
+---