Instructions to use iterateai/Interplay-AppCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iterateai/Interplay-AppCoder with Transformers:

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

pipe = pipeline("text-generation", model="iterateai/Interplay-AppCoder")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iterateai/Interplay-AppCoder")
model = AutoModelForCausalLM.from_pretrained("iterateai/Interplay-AppCoder")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use iterateai/Interplay-AppCoder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/iterateai/Interplay-AppCoder

SGLang

How to use iterateai/Interplay-AppCoder 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 "iterateai/Interplay-AppCoder" \
    --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": "iterateai/Interplay-AppCoder",
		"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 "iterateai/Interplay-AppCoder" \
        --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": "iterateai/Interplay-AppCoder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use iterateai/Interplay-AppCoder with Docker Model Runner:
```
docker model run hf.co/iterateai/Interplay-AppCoder
```

iterateai commited on Oct 31, 2023

Commit

ffdcf97

1 Parent(s): e43b534

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@@ -5,6 +5,7 @@ tags:
 - langchain
 - python
 - yolov8
 ---
 # Interplay-AppCoder a CodeGeneration LLM
 **Iterate’s new top-performing Interplay-AppCoder LLM scores 2.9 on usefulness and 2.7 on functionality on the ICE Benchmark Test**
@@ -25,7 +26,7 @@ The result is Interplay-AppCoder LLM, a brand new high performing code generatio
 - **Developed by:** [Iterate.ai]
-- **Language(s) (NLP):** [Python,Langchain,yolov8]
 - **Finetuned from model :** [Wizardcoder-15B-v1.0]
 ### Model Sources [optional]
@@ -129,7 +130,11 @@ You can read more about the ICE methodology in this paper.

 - langchain
 - python
 - yolov8
+- vertexai
 ---
 # Interplay-AppCoder a CodeGeneration LLM
 **Iterate’s new top-performing Interplay-AppCoder LLM scores 2.9 on usefulness and 2.7 on functionality on the ICE Benchmark Test**
 - **Developed by:** [Iterate.ai]
+- **Language(s) (NLP):** [Python,Langchain,yolov8,vertexai]
 - **Finetuned from model :** [Wizardcoder-15B-v1.0]
 ### Model Sources [optional]
+## Can you try it?
+Yes, we’ve opened it up. Try out yourself right here:
+* Can you provide a python script that uses the YOLOv8 model from the Ultralytics library to detect people in an image, draw green bounding boxes around them, and then save the image?
+* Write a python code using langchain to do Question and Answering over a blog post.
+* Write a python code using langchain library to retrieve information from SQL database and a vector store
+* How can I set up clients for job service, model service, endpoint service, and prediction service using the Vertex AI client library in Python?