Instructions to use beowolx/CodeNinja-1.0-OpenChat-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beowolx/CodeNinja-1.0-OpenChat-7B with Transformers:

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

pipe = pipeline("text-generation", model="beowolx/CodeNinja-1.0-OpenChat-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("beowolx/CodeNinja-1.0-OpenChat-7B")
model = AutoModelForCausalLM.from_pretrained("beowolx/CodeNinja-1.0-OpenChat-7B", device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use beowolx/CodeNinja-1.0-OpenChat-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beowolx/CodeNinja-1.0-OpenChat-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beowolx/CodeNinja-1.0-OpenChat-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/beowolx/CodeNinja-1.0-OpenChat-7B

SGLang

How to use beowolx/CodeNinja-1.0-OpenChat-7B 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 "beowolx/CodeNinja-1.0-OpenChat-7B" \
    --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": "beowolx/CodeNinja-1.0-OpenChat-7B",
		"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 "beowolx/CodeNinja-1.0-OpenChat-7B" \
        --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": "beowolx/CodeNinja-1.0-OpenChat-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beowolx/CodeNinja-1.0-OpenChat-7B with Docker Model Runner:
```
docker model run hf.co/beowolx/CodeNinja-1.0-OpenChat-7B
```

beowolx commited on Dec 22, 2023

Commit

9934c04

1 Parent(s): b1f6b06

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@@ -22,7 +22,7 @@ tags:
 ## Overview
-CodeNinja is an enhanced version of the renowned model [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210). It represents a breakthrough in coding assistance, having been fine-tuned through Supervised Fine Tuning on two expansive datasets, encompassing over 400,000 coding instructions. Designed to be an indispensable tool for coders, CodeNinja aims to integrate seamlessly into your daily coding routine.
 Discover the quantized versions at: [beowolx/CodeNinja-1.0-OpenChat-7B-GGUF](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B-GGUF).
@@ -32,8 +32,6 @@ Discover the quantized versions at: [beowolx/CodeNinja-1.0-OpenChat-7B-GGUF](htt
 - **Flexibility and Scalability**: Available in a 7B model size, CodeNinja is adaptable for local runtime environments.
-- **Exceptional Performance**: Achieves top-tier results among publicly accessible coding models, particularly notable on benchmarks like HumanEval.
 - **Advanced Code Completion**: With a substantial context window size of 8192, it supports comprehensive project-level code completion.
 ## Prompt Format

 ## Overview
+CodeNinja is an enhanced version of the renowned model [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210). It having been fine-tuned through Supervised Fine Tuning on two expansive datasets, encompassing over 400,000 coding instructions. Designed to be an indispensable tool for coders, CodeNinja aims to integrate seamlessly into your daily coding routine.
 Discover the quantized versions at: [beowolx/CodeNinja-1.0-OpenChat-7B-GGUF](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B-GGUF).
 - **Flexibility and Scalability**: Available in a 7B model size, CodeNinja is adaptable for local runtime environments.
 - **Advanced Code Completion**: With a substantial context window size of 8192, it supports comprehensive project-level code completion.
 ## Prompt Format