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 Settings

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
```

QagentS commited on May 13, 2024

Commit

8f86f9d

verified ·

1 Parent(s): 2ff8a9c

Update README.md

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README.md +16 -6

README.md CHANGED Viewed

@@ -45,10 +45,15 @@ widget:
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658d8095a2a6a6e0da8bb8a6/V5vUqiFiV4DPt1Xm77PHv.png)
-Given a goal and tools can Ai intelligently use the tools to reach the goal ? \n
-What if it has a meagre 1.3b params/neurons akin to that of an owl ? \n
-Can it follow instructions and plan to reach a goal ? \n
-Apparently it can. \n
 Releasing `pip-code-bandit` and `pip_flow` a model and a library to manage and run goal oriented agentic system.
@@ -67,6 +72,7 @@ Releasing `pip-code-bandit` and `pip_flow` a model and a library to manage and r
 6. code generation | doc
 7. file recreated in json | any raw data
 8. corrected generation | new instruction with error
 -- instruction following , RL tuned.
 ```
@@ -79,8 +85,9 @@ The reward policy is around the concept of model going to a stable state of zero
 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
 The model is open source under apache 2.0. License
 ## Usage
@@ -115,4 +122,7 @@ pip install transformers
 ### Team
-Avi Kothari, Gyan Ranjan, Pratham Gupta, Ritvik Aryan Kalra, Soham Acharya

 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658d8095a2a6a6e0da8bb8a6/V5vUqiFiV4DPt1Xm77PHv.png)
+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 `pip_flow` a model and a library to manage and run goal oriented agentic system.
 6. code generation | doc
 7. file recreated in json | any raw data
 8. corrected generation | new instruction with error
 -- instruction following , RL tuned.
 ```
 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
 The model is open source under apache 2.0. License
+```
 ## Usage
 ### Team
+```doc
+Avi Kothari, Gyan Ranjan, Pratham Gupta, Ritvik Aryan Kalra, Soham Acharya
+```