Instructions to use microsoft/NextCoder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/NextCoder-7B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/NextCoder-7B")
model = AutoModelForCausalLM.from_pretrained("microsoft/NextCoder-7B")
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
Local Apps Settings

vLLM

How to use microsoft/NextCoder-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/NextCoder-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": "microsoft/NextCoder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/microsoft/NextCoder-7B

SGLang

How to use microsoft/NextCoder-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 "microsoft/NextCoder-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": "microsoft/NextCoder-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 "microsoft/NextCoder-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": "microsoft/NextCoder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use microsoft/NextCoder-7B with Docker Model Runner:
```
docker model run hf.co/microsoft/NextCoder-7B
```

rootacess commited on May 3, 2025

Commit

2094836

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1 Parent(s): cd4e0ef

Update README.md

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@@ -22,7 +22,7 @@ datasets:
         <a href="https://github.com/microsoft/NextCoder">GitHub</a>&nbsp&nbsp | &nbsp&nbsp <a href="https://arxiv.org/abs/2503.03656">Arxiv</a>
 </p>
-> Published in ICML'2025
 ## Introduction
@@ -94,7 +94,7 @@ generated_ids = [
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
-## Evaluation and Performanc
 | Models | HUMANEVALEDIT | CANITEDIT | AIDER | POLYGLOT |
 |--------|---------------|-----------|-------|----------|

         <a href="https://github.com/microsoft/NextCoder">GitHub</a>&nbsp&nbsp | &nbsp&nbsp <a href="https://arxiv.org/abs/2503.03656">Arxiv</a>
 </p>
+> NextCoder: Robust Adaptation of Code LMs to Diverse Code Edits (ICML'2025)
 ## Introduction
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
+## Evaluation and Performance
 | Models | HUMANEVALEDIT | CANITEDIT | AIDER | POLYGLOT |
 |--------|---------------|-----------|-------|----------|