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

82f5414

1 Parent(s): 9230022

Update README.md

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README.md +4 -2

README.md CHANGED Viewed

@@ -47,6 +47,7 @@ The model is optimized for code generation and cannot be used as chat model.
 ## How to Get Started with the Model
 Use the code below to get started with the model.
 #import model from hugging face repository
 import torch
 from transformers import (
@@ -58,8 +59,9 @@ from transformers import (
     logging
 )
 model_repo_id ="iterateai/Interplay-AppCoder"
 #### Load the model in FP16
 iterate_model = AutoModelForCausalLM.from_pretrained(
     model_repo_id,
     low_cpu_mem_usage=True,
@@ -69,7 +71,7 @@ iterate_model = AutoModelForCausalLM.from_pretrained(
     trust_remote_code=True
 )
 #Note: You can quantize the model using bnb confi parameter to load the model in T4 GPU
 ### Load tokenizer to save it
 tokenizer = AutoTokenizer.from_pretrained(model_repo_id, trust_remote_code=True)
 tokenizer.pad_token = tokenizer.eos_token

 ## How to Get Started with the Model
 Use the code below to get started with the model.
+```
 #import model from hugging face repository
 import torch
 from transformers import (
     logging
 )
 model_repo_id ="iterateai/Interplay-AppCoder"
+```
 #### Load the model in FP16
+```
 iterate_model = AutoModelForCausalLM.from_pretrained(
     model_repo_id,
     low_cpu_mem_usage=True,
     trust_remote_code=True
 )
 #Note: You can quantize the model using bnb confi parameter to load the model in T4 GPU
+```
 ### Load tokenizer to save it
 tokenizer = AutoTokenizer.from_pretrained(model_repo_id, trust_remote_code=True)
 tokenizer.pad_token = tokenizer.eos_token