Instructions to use PipableAI/pip-code-bandit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PipableAI/pip-code-bandit with Transformers:

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

pipe = pipeline("text-generation", model="PipableAI/pip-code-bandit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-code-bandit")
model = AutoModelForCausalLM.from_pretrained("PipableAI/pip-code-bandit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PipableAI/pip-code-bandit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PipableAI/pip-code-bandit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PipableAI/pip-code-bandit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PipableAI/pip-code-bandit

SGLang

How to use PipableAI/pip-code-bandit 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 "PipableAI/pip-code-bandit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PipableAI/pip-code-bandit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "PipableAI/pip-code-bandit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PipableAI/pip-code-bandit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PipableAI/pip-code-bandit with Docker Model Runner:
```
docker model run hf.co/PipableAI/pip-code-bandit
```

rvk7895 commited on May 13, 2024

Commit

83dd665

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@@ -32,7 +32,7 @@ widget:
 ---
 # pip-code-bandit
-[pipableAi](https://www.pipable.ai/)
 [colab_notebook](https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing)
@@ -51,9 +51,9 @@ widget:
 Given a goal and tools, can AI intelligently use the tools to reach the goal?\
 What if it has a meagre 1.3b params/neurons akin to that of an owl? Can it follow instructions and plan to reach a goal?\
-Apparently it can!\
 Releasing **pip-code-bandit** and **pipflow**\
-A `model` and a `library` to manage and run goal oriented agentic system.
 ## Model attributes
@@ -77,16 +77,16 @@ A `model` and a `library` to manage and run goal oriented agentic system.
 ```
-## How we built it?
 We used a simulator to simulate environments where the model could play games to achieve goals, given a set of actions available to it.
-All the model could do was find the right action and config to incur positive reward.
-The reward policy is around the concept of model going to a stable state of zero net sum reward for both good and bad behaviour.
-In this set up the model, which was pre trained on code , function documentation and similar OS datasets ,was RL tuned for instruction following and reliability.
 ## License
 ```bash
-complete open sourced - apache 2.0. License
 ```
 ## Usage
@@ -124,15 +124,15 @@ curl -X 'POST' \
   -d 'model_name=PipableAI%2Fpip-code-bandit&prompt="YOUR PROMPT"&max_new_tokens=400'
 ```
-Alternatively, you can directly access UI endpoint at https://playground.pipable.ai/docs#/default/infer_infer_post.
 ### Library Usage
-For directly using the capabilities of model without putting extra efforts on schemas and prompts try to use [pipflow](https://github.com/PipableAI/pipflow).
-For detailed usage refer to the [colab_notebook](https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing)

 ---
 # pip-code-bandit
+[PipableAI](https://www.pipable.ai/)
 [colab_notebook](https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing)
 Given a goal and tools, can AI intelligently use the tools to reach the goal?\
 What if it has a meagre 1.3b params/neurons akin to that of an owl? Can it follow instructions and plan to reach a goal?\
+It can!\
 Releasing **pip-code-bandit** and **pipflow**\
+A `model` and a `library` to manage and run goal-oriented agentic system.
 ## Model attributes
 ```
+## How did we build it?
 We used a simulator to simulate environments where the model could play games to achieve goals, given a set of actions available to it.
+All the model could do was find the right action and config to incur a positive reward.
+The reward policy is around the concept of a model going to a stable state of zero net sum reward for both good and bad behaviour.
+In this setup, the model, which was pre-trained on code, function documentation, and similar OS datasets, was RL-tuned for reliability and instruction-following.
 ## License
 ```bash
+complete open-sourced - apache 2.0. License
 ```
 ## Usage
   -d 'model_name=PipableAI%2Fpip-code-bandit&prompt="YOUR PROMPT"&max_new_tokens=400'
 ```
+Alternatively, you can directly access the UI endpoint at https://playground.pipable.ai/docs#/default/infer_infer_post.
 ### Library Usage
+To directly use the model's capabilities without putting extra effort into schemas and prompts, try to use [pipflow](https://github.com/PipableAI/pipflow).
+For detailed usage, refer to the [colab_notebook](https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing)