Instructions to use replit/replit-code-v1-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use replit/replit-code-v1-3b with Transformers:

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

pipe = pipeline("text-generation", model="replit/replit-code-v1-3b", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("replit/replit-code-v1-3b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("replit/replit-code-v1-3b", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use replit/replit-code-v1-3b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/replit/replit-code-v1-3b

SGLang

How to use replit/replit-code-v1-3b 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 "replit/replit-code-v1-3b" \
    --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": "replit/replit-code-v1-3b",
		"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 "replit/replit-code-v1-3b" \
        --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": "replit/replit-code-v1-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use replit/replit-code-v1-3b with Docker Model Runner:
```
docker model run hf.co/replit/replit-code-v1-3b
```

Update README.md

by pirroh - opened May 3, 2023

base: refs/heads/main

←

from: refs/pr/5

Discussion Files changed

-2

Files changed (1) hide show

README.md +7 -2

README.md CHANGED Viewed

@@ -95,8 +95,13 @@ from transformers import AutoModelForCausalLM
 model = AutoModelForCausalLM.from_pretrained('replit/replit-code-v1-3b', trust_remote_code=True)
 ```
-To use the optimized Triton implementation of FlashAttention on GPUs with BF16 precision, move the model to `bfloat16` and use it as follows:
 ```python
 from transformers import AutoModelForCausalLM
@@ -106,7 +111,7 @@ model.to(device='cuda:0', dtype=torch.bfloat16)
 # forward pass
 x = torch.tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])
-x = x.to(device='cuda:0', dtype=torch.bfloat16)
 y = model(x)
 ```

 model = AutoModelForCausalLM.from_pretrained('replit/replit-code-v1-3b', trust_remote_code=True)
 ```
+To use the optimized Triton implementation of FlashAttention on GPUs with BF16 precision, first install the following dependencies:
+```
+flash-attn==0.2.8
+triton==2.0.0.dev20221202
+```
+Then, move the model to `bfloat16` and use it as follows:
 ```python
 from transformers import AutoModelForCausalLM
 # forward pass
 x = torch.tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])
+x = x.to(device='cuda:0')
 y = model(x)
 ```