Instructions to use teknium/Replit-v1-CodeInstruct-3B-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teknium/Replit-v1-CodeInstruct-3B-fp16 with Transformers:

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

pipe = pipeline("text-generation", model="teknium/Replit-v1-CodeInstruct-3B-fp16", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("teknium/Replit-v1-CodeInstruct-3B-fp16", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("teknium/Replit-v1-CodeInstruct-3B-fp16", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use teknium/Replit-v1-CodeInstruct-3B-fp16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teknium/Replit-v1-CodeInstruct-3B-fp16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teknium/Replit-v1-CodeInstruct-3B-fp16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/teknium/Replit-v1-CodeInstruct-3B-fp16

SGLang

How to use teknium/Replit-v1-CodeInstruct-3B-fp16 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 "teknium/Replit-v1-CodeInstruct-3B-fp16" \
    --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": "teknium/Replit-v1-CodeInstruct-3B-fp16",
		"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 "teknium/Replit-v1-CodeInstruct-3B-fp16" \
        --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": "teknium/Replit-v1-CodeInstruct-3B-fp16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use teknium/Replit-v1-CodeInstruct-3B-fp16 with Docker Model Runner:
```
docker model run hf.co/teknium/Replit-v1-CodeInstruct-3B-fp16
```

Replit-v1-CodeInstruct-3B-fp16

Commit History

Enable 8/4bit quantization

3bbb04a

teknium commited on Jun 7, 2023

Fixing path issues to fix loading of models with Auto* classes. (#1)

e340b69

teknium

madhavatreplit commited on May 28, 2023

Update README.md

e5a060f

teknium commited on May 27, 2023

Create README.md

65695e9

teknium commited on May 26, 2023

Upload 19 files

8bba98a

teknium commited on May 26, 2023

initial commit

9441baf

teknium commited on May 26, 2023

Commit History

Enable 8/4bit quantization 3bbb04a

Fixing path issues to fix loading of models with Auto* classes. (#1) e340b69

Update README.md e5a060f

Create README.md 65695e9

Upload 19 files 8bba98a

initial commit 9441baf

Enable 8/4bit quantization

3bbb04a

Fixing path issues to fix loading of models with Auto* classes. (#1)

e340b69

Update README.md

e5a060f

Create README.md

65695e9

Upload 19 files

8bba98a

initial commit

9441baf