Instructions to use Salesforce/CoDA-v0-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Salesforce/CoDA-v0-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Salesforce/CoDA-v0-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Salesforce/CoDA-v0-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Salesforce/CoDA-v0-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Salesforce/CoDA-v0-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Salesforce/CoDA-v0-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Salesforce/CoDA-v0-Instruct

SGLang

How to use Salesforce/CoDA-v0-Instruct 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 "Salesforce/CoDA-v0-Instruct" \
    --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": "Salesforce/CoDA-v0-Instruct",
		"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 "Salesforce/CoDA-v0-Instruct" \
        --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": "Salesforce/CoDA-v0-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Salesforce/CoDA-v0-Instruct with Docker Model Runner:
```
docker model run hf.co/Salesforce/CoDA-v0-Instruct
```

hlnchen commited on Oct 3, 2025

Commit

6c86734

verified ·

1 Parent(s): 99522cf

Update README.md

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README.md +26 -4

README.md CHANGED Viewed

@@ -11,12 +11,13 @@ tags:
 # CoDA-v0-Instruct
 ## Overview
-CoDA is Salesforce AI Research's open, lightweight and diffusion-based language model.
 [Technical Report](https://github.com/SalesforceAIResearch/CoDA/blob/main/technical_report.pdf)
 [Code](https://github.com/SalesforceAIResearch/CoDA/)
 ## Requirements
@@ -64,7 +65,28 @@ generated_ids = [
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
-### Deployment
-For deployment, please checkout our repo.
 ## Citation

 # CoDA-v0-Instruct
 ## Overview
+CoDA is Salesforce AI Research's open diffusion language model.
 [Technical Report](https://github.com/SalesforceAIResearch/CoDA/blob/main/technical_report.pdf)
 [Code](https://github.com/SalesforceAIResearch/CoDA/)
+The code repo contains a unified training pipeline from pre-training to post-training, evaluation harnesses, and a simple Fast-API based serving backend.
 ## Requirements
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
+## Benchmark 📊
+Comparison of code-generation performance across standard and plus-enhanced benchmarks. Evalplus is computed as the mean pass@1 on enhanced variants. Bold marks results where CoDA produces the strongest diffusion-model performance.
+| Model | Humaneval Instruct | Humaneval Plus | MBPP Instruct | MBPP Plus | Evalplus |
+| --- | --- | --- | --- | --- | --- |
+| CoDA-Base | 29.3 | 23.8 | 35.2 | 46.0 | 34.9 |
+| CoDA-Instruct | 54.3 | 47.6 | 47.2 | **63.2** | **55.4** |
+| Dream-Base | 56.7 | 50.0 | 68.7 | 57.4 | 53.7 |
+| Dream-7B-Instruct | 57.9 | 53.7 | 68.3 | 56.1 | 54.9 |
+| LLaDA-8B-Instruct | 35.4 | 31.7 | 31.5 | 28.6 | 30.2 |
+| Qwen3-1.7B | 66.5 | 61.6 | 46.2 | 65.9 | 63.8 |
+| Qwen2.5-Coder-1.5B | 43.9 | 36.6 | 69.2 | 58.6 | 47.6 |
+| Qwen2.5-Coder-1.5B-Instruct | 70.7 | 66.5 | 69.2 | 59.4 | 62.3 |
+| Gemma-3-1B-it | 39.6 | 35.4 | 39.4 | 63.5 | 49.5 |
+| LLaMA-3.2-1B-Instruct | 35.4 | 31.1 | 24.4 | 53.7 | 42.4 |
+## Deployment
+Checkout our [Deployment Guide](https://github.com/SalesforceAIResearch/CoDA?tab=readme-ov-file#deployment-guide-%EF%B8%8F)!
 ## Citation
+```
+coming soon
+```