Instructions to use TechxGenus/CursorCore-DS-1.3B-SR-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TechxGenus/CursorCore-DS-1.3B-SR-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="TechxGenus/CursorCore-DS-1.3B-SR-GPTQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-DS-1.3B-SR-GPTQ")
model = AutoModelForCausalLM.from_pretrained("TechxGenus/CursorCore-DS-1.3B-SR-GPTQ")
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 TechxGenus/CursorCore-DS-1.3B-SR-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TechxGenus/CursorCore-DS-1.3B-SR-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TechxGenus/CursorCore-DS-1.3B-SR-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TechxGenus/CursorCore-DS-1.3B-SR-GPTQ

SGLang

How to use TechxGenus/CursorCore-DS-1.3B-SR-GPTQ 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 "TechxGenus/CursorCore-DS-1.3B-SR-GPTQ" \
    --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": "TechxGenus/CursorCore-DS-1.3B-SR-GPTQ",
		"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 "TechxGenus/CursorCore-DS-1.3B-SR-GPTQ" \
        --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": "TechxGenus/CursorCore-DS-1.3B-SR-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TechxGenus/CursorCore-DS-1.3B-SR-GPTQ with Docker Model Runner:
```
docker model run hf.co/TechxGenus/CursorCore-DS-1.3B-SR-GPTQ
```

TechxGenus commited on Oct 10, 2024

Commit

f0be2fb

verified ·

1 Parent(s): 0f011fd

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@@ -17,8 +17,8 @@ license_link: LICENSE
 <a href="https://hf.co/papers/2410.07002">[🤗HF Paper]</a> |
 <a href="https://huggingface.co/collections/TechxGenus/cursorcore-series-6706618c38598468866b60e2">[🤖Models]</a> |
 <a href="https://github.com/TechxGenus/CursorCore">[🛠️Code]</a> |
-<a href="https://github.com/TechxGenus/CursorWeb">[<img src="https://github.com/TechxGenus/CursorCore/blob/main/pictures/cursorcore.png" width="12.5px">Web]</a> |
-<a href="https://discord.gg/Z5Tev8fV">[<img src="https://github.com/TechxGenus/CursorCore/blob/main/pictures/discord.png" width="15x">Discord]</a>
 </p>
 <hr>
@@ -41,10 +41,10 @@ license_link: LICENSE
 CursorCore is a series of open-source models designed for AI-assisted programming. It aims to support features such as automated editing and inline chat, replicating the core abilities of closed-source AI-assisted programming tools like Cursor. This is achieved by aligning data generated through Programming-Instruct. Please read [our paper](http://arxiv.org/abs/2410.07002) to learn more.
 <p align="center">
-<img width="100%" alt="conversation" src="https://github.com/TechxGenus/CursorCore/blob/main/pictures/conversation.png">
 </p>
-![CursorWeb](https://github.com/TechxGenus/CursorCore/blob/main/pictures/CursorWeb.gif)
 ## Models

 <a href="https://hf.co/papers/2410.07002">[🤗HF Paper]</a> |
 <a href="https://huggingface.co/collections/TechxGenus/cursorcore-series-6706618c38598468866b60e2">[🤖Models]</a> |
 <a href="https://github.com/TechxGenus/CursorCore">[🛠️Code]</a> |
+<a href="https://github.com/TechxGenus/CursorWeb">[Web]</a> |
+<a href="https://discord.gg/Z5Tev8fV">[Discord]</a>
 </p>
 <hr>
 CursorCore is a series of open-source models designed for AI-assisted programming. It aims to support features such as automated editing and inline chat, replicating the core abilities of closed-source AI-assisted programming tools like Cursor. This is achieved by aligning data generated through Programming-Instruct. Please read [our paper](http://arxiv.org/abs/2410.07002) to learn more.
 <p align="center">
+<img width="100%" alt="conversation" src="https://raw.githubusercontent.com/TechxGenus/CursorCore/main/pictures/conversation.png">
 </p>
+![CursorWeb](https://raw.githubusercontent.com/TechxGenus/CursorCore/main/pictures/CursorWeb.gif)
 ## Models