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

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@@ -8,6 +8,8 @@ tags:
 ---
 # 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**
 The world of LLMs is growing rapidly as Several new LLMs and finetunes are released daily by the open-source community, startups, and enterprises, as new models are invented to perform various novel tasks.
 One of our Iterate.ai R&D Projects has been to experiment with several LLMs, then train a code generation LLM on the latest generative AI frameworks and libraries. Our goal was to generate working and updated code for generative AI projects that we build alongside our enterprise clients.
@@ -19,49 +21,22 @@ The result is Interplay-AppCoder LLM, a brand new high performing code generatio
 ## Model Details
-The base model which Interplay-Appcoder is based on: Wizardcoder-15B-v1.0
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 ### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
 ## Bias, Risks, and Limitations

 ---
 # 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**
 The world of LLMs is growing rapidly as Several new LLMs and finetunes are released daily by the open-source community, startups, and enterprises, as new models are invented to perform various novel tasks.
 One of our Iterate.ai R&D Projects has been to experiment with several LLMs, then train a code generation LLM on the latest generative AI frameworks and libraries. Our goal was to generate working and updated code for generative AI projects that we build alongside our enterprise clients.
 ## Model Details
+- **Developed by:** [Iterate.ai]
+- **Language(s) (NLP):** [Python,Langchain,yolov8]
+- **Finetuned from model :** [Wizardcoder-15B-v1.0]
 ### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
+- **Demo [optional]:** [https://appcoder.interplay.iterate.ai/]
 ## Bias, Risks, and Limitations