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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

teknium commited on May 26, 2023

Commit

64a7614

1 Parent(s): fa60a9a

Update README.md

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@@ -68,6 +68,18 @@ or
 ```
 This model for me produced coherent outputs with the following sampler settings, but feel free to experiment:
 ```
 max_new_tokens=128, do_sample=True, use_cache=True, temperature=0.2, top_p=0.9, eos_token_id= self.tokenizer.eos_token_id

 ```
+This model seems to have issues with device="auto" in the model arguments (and requires the trust_remote_code=True, so you should maybe load it like I am here:
+```
+        self.tokenizer = AutoTokenizer.from_pretrained("./Replit-CodeInstruct/", trust_remote_code=True)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            "./Replit-CodeInstruct",
+            torch_dtype=torch.bfloat16,
+            trust_remote_code=True
+        )
+        self.model.to('cuda')
+```
 This model for me produced coherent outputs with the following sampler settings, but feel free to experiment:
 ```
 max_new_tokens=128, do_sample=True, use_cache=True, temperature=0.2, top_p=0.9, eos_token_id= self.tokenizer.eos_token_id