Instructions to use microsoft/NextCoder-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/NextCoder-14B with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/NextCoder-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/NextCoder-14B")
model = AutoModelForCausalLM.from_pretrained("microsoft/NextCoder-14B")
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

vLLM

How to use microsoft/NextCoder-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/NextCoder-14B"
# 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-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/microsoft/NextCoder-14B

SGLang

How to use microsoft/NextCoder-14B 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-14B" \
    --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-14B",
		"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-14B" \
        --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-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use microsoft/NextCoder-14B with Docker Model Runner:
```
docker model run hf.co/microsoft/NextCoder-14B
```

rootacess commited on Jun 10, 2025

Commit

f1f74b6

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Update README.md

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@@ -97,12 +97,16 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
 ## Evaluation and Performance
-| Models | HUMANEVALEDIT | CANITEDIT | AIDER | POLYGLOT |
 |--------|---------------|-----------|-------|----------|
 | QwenCoder-2.5-3B | 73.2 | 37.1 | 36.8 | - |
 | QwenCoder-2.5-3B-LoRA | 64.6 | 36.2 | 35.8 | - |
 | QwenCoder-2.5-3B-SFT | 76.2 | 32.4 | 30.1 | - |
 | **NextCoder-3B** | 75.6 | 42.4 | 37.6 | - |
 | QwenCoder-2.5-14B | 87.8 | 58.1 | 66.9 | 9.3 |
 | QwenCoder-2.5-14B-LoRA | 78.0 | 50.9 | 66.2 | 5.3 |
 | QwenCoder-2.5-14B-SFT | 79.9 | 42.4 | 36.8 | 3.1 |
@@ -119,13 +123,11 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ## Citation
 ```bibtex
-@misc{aggarwal2025robustlearningdiversecode,
-      title={Robust Learning of Diverse Code Edits},
-      author={Tushar Aggarwal and Swayam Singh and Abhijeet Awasthi and Aditya Kanade and Nagarajan Natarajan},
-      year={2025},
-      eprint={2503.03656},
-      archivePrefix={arXiv},
-      primaryClass={cs.SE},
-      url={https://arxiv.org/abs/2503.03656},
 }
 ```

 ```
 ## Evaluation and Performance
+| Models | HUMANEVALFIX | CANITEDIT | AIDER | POLYGLOT |
 |--------|---------------|-----------|-------|----------|
 | QwenCoder-2.5-3B | 73.2 | 37.1 | 36.8 | - |
 | QwenCoder-2.5-3B-LoRA | 64.6 | 36.2 | 35.8 | - |
 | QwenCoder-2.5-3B-SFT | 76.2 | 32.4 | 30.1 | - |
 | **NextCoder-3B** | 75.6 | 42.4 | 37.6 | - |
+| QwenCoder-2.5-7B | 73.8 | 48.1 | 59.4 | - |
+| QwenCoder-2.5-7B-LoRA | 70.7 | 44.3 | 40.6 | - |
+| QwenCoder-2.5-7B-SFT | 70.1 | 36.7 | 48.9 | - |
+| **NextCoder-7B** | 81.1 | 50.5 | 65.7 | - |
 | QwenCoder-2.5-14B | 87.8 | 58.1 | 66.9 | 9.3 |
 | QwenCoder-2.5-14B-LoRA | 78.0 | 50.9 | 66.2 | 5.3 |
 | QwenCoder-2.5-14B-SFT | 79.9 | 42.4 | 36.8 | 3.1 |
 ## Citation
 ```bibtex
+@inproceedings{aggarwal2025nextcoder,
+author = {Aggarwal, Tushar and Singh, Swayam and Awasthi, Abhijeet and Kanade, Aditya and Natarajan, Nagarajan},
+title = {NextCoder: Robust Adaptation of Code LMs to Diverse Code Edits},
+booktitle = {International Conference on Machine Learning},
+year = {2025},
+url = {https://www.microsoft.com/en-us/research/publication/nextcoder-robust-adaptation-of-code-lms-to-diverse-code-edits/},
 }
 ```