Instructions to use continuedev/instinct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use continuedev/instinct with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("continuedev/instinct")
model = AutoModelForCausalLM.from_pretrained("continuedev/instinct")
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 continuedev/instinct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/continuedev/instinct

SGLang

How to use continuedev/instinct 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 "continuedev/instinct" \
    --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": "continuedev/instinct",
		"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 "continuedev/instinct" \
        --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": "continuedev/instinct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use continuedev/instinct with Docker Model Runner:
```
docker model run hf.co/continuedev/instinct
```

instinct

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---
license: apache-2.0
datasets:
- continuedev/instinct-data
base_model:
- Qwen/Qwen2.5-Coder-7B
pipeline_tag: text-generation
library_name: transformers
---

<img src="https://cdn-uploads.huggingface.co/production/uploads/686c5c546abedce0f7ac048a/B7PeaDQCDnlgT3Tmf7fsb.png" width=250>

# Instinct, the State-of-the-Art Open Next Edit Model

This repo contains the model weights for [Continue](https://continue.dev)'s state-of-the-art open Next Edit model, **Instinct**. Robustly fine-tuned from Qwen2.5-Coder-7B on our [dataset of real-world code edits](https://huggingface.co/datasets/continuedev/instinct-data), Instinct intelligently predicts your next move to keep you in flow. 

## Serving the model

**Ollama**: We've released a [Q4_K_M GGUF quantization of Instinct](https://huggingface.co/continuedev/instinct-GGUF) for efficient local inference. Try it with [Continue's Ollama integration](https://docs.continue.dev/guides/ollama-guide), or just run `ollama run nate/instinct`.

You can also serve the model using either of the below options, then [connect it with Continue](https://docs.continue.dev/guides/how-to-self-host-a-model).

**SGLang**: `python3 -m sglang.launch_server --model-path continuedev/instinct --load-format safetensors`
<br>**vLLM**: `vllm serve continuedev/instinct --served-model-name instinct --load-format safetensors`

## Learn more

For more information on the work behind Instinct, please refer to our [blog](https://blog.continue.dev/instinct/).