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
```

QagentS commited on May 13, 2024

Commit

bb86d19

verified ·

1 Parent(s): a596058

Upload LlamaForCausalLM

Browse files

Files changed (2) hide show

README.md +12 -2
config.json +1 -1

README.md CHANGED Viewed

@@ -15,7 +15,7 @@ tags:
 - function calling
 - unit tests
 - causalLM
-- codeLLAMA
 - instruction_tuned
 - basemodel
 - pytorch
@@ -26,7 +26,17 @@ metrics:
 - accuracy
 pipeline_tag: text-generation
 widget:
-- text: '<function_code>def _plot_bounding_polygon(polygons_coordinates, output_html_path=bounding_polygon_map.html):map_center = [sum([coord[0]for polygon_coords in polygons_coordinatesfor coord in polygon_coords])/ sum([len(polygon_coords) for polygon_coords in polygons_coordinates]),sum([coord[1]for polygon_coords in polygons_coordinatesfor coord in polygon_coords])/ sum([len(polygon_coords) for polygon_coords in polygons_coordinates]),]my_map = folium.Map(location=map_center, zoom_start=12)for polygon_coords in polygons_coordinates:folium.Polygon(locations=polygon_coords,color=blue,fill=True,fill_color=blue,fill_opacity=0.2,).add_to(my_map)marker_cluster = MarkerCluster().add_to(my_map)for polygon_coords in polygons_coordinates:for coord in polygon_coords:folium.Marker(location=[coord[0], coord[1]], popup=fCoordinates: {coord}).add_to(marker_cluster)draw = Draw(export=True)draw.add_to(my_map)my_map.save(output_html_path)return output_html_path</function_code><question>Document the python code above giving function description ,parameters and return type and example how to call the function</question><doc>'
   example_title: example
 ---
 # pip-library-etl-1.3b

 - function calling
 - unit tests
 - causalLM
+- codeLLAMA
 - instruction_tuned
 - basemodel
 - pytorch
 - accuracy
 pipeline_tag: text-generation
 widget:
+- text: '<function_code>def _plot_bounding_polygon(polygons_coordinates, output_html_path=bounding_polygon_map.html):map_center
+    = [sum([coord[0]for polygon_coords in polygons_coordinatesfor coord in polygon_coords])/
+    sum([len(polygon_coords) for polygon_coords in polygons_coordinates]),sum([coord[1]for
+    polygon_coords in polygons_coordinatesfor coord in polygon_coords])/ sum([len(polygon_coords)
+    for polygon_coords in polygons_coordinates]),]my_map = folium.Map(location=map_center,
+    zoom_start=12)for polygon_coords in polygons_coordinates:folium.Polygon(locations=polygon_coords,color=blue,fill=True,fill_color=blue,fill_opacity=0.2,).add_to(my_map)marker_cluster
+    = MarkerCluster().add_to(my_map)for polygon_coords in polygons_coordinates:for
+    coord in polygon_coords:folium.Marker(location=[coord[0], coord[1]], popup=fCoordinates:
+    {coord}).add_to(marker_cluster)draw = Draw(export=True)draw.add_to(my_map)my_map.save(output_html_path)return
+    output_html_path</function_code><question>Document the python code above giving
+    function description ,parameters and return type and example how to call the function</question><doc>'
   example_title: example
 ---
 # pip-library-etl-1.3b

config.json CHANGED Viewed

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "/content/drive/My Drive/pip-code-bandit-275000/",
   "architectures": [
     "LlamaForCausalLM"
   ],

 {
+  "_name_or_path": "PipableAI/pip-code-bandi-testv1",
   "architectures": [
     "LlamaForCausalLM"
   ],