Instructions to use zyoralabs/Zyora-DEV-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zyoralabs/Zyora-DEV-32B with Transformers:

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

pipe = pipeline("text-generation", model="zyoralabs/Zyora-DEV-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zyoralabs/Zyora-DEV-32B")
model = AutoModelForCausalLM.from_pretrained("zyoralabs/Zyora-DEV-32B")
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 zyoralabs/Zyora-DEV-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zyoralabs/Zyora-DEV-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zyoralabs/Zyora-DEV-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zyoralabs/Zyora-DEV-32B

SGLang

How to use zyoralabs/Zyora-DEV-32B 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 "zyoralabs/Zyora-DEV-32B" \
    --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": "zyoralabs/Zyora-DEV-32B",
		"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 "zyoralabs/Zyora-DEV-32B" \
        --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": "zyoralabs/Zyora-DEV-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zyoralabs/Zyora-DEV-32B with Docker Model Runner:
```
docker model run hf.co/zyoralabs/Zyora-DEV-32B
```

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Zyora-DEV-32B

Code Generation & Security Scanning Model

Try the API • Documentation • Website

Model Description

Zyora-DEV-32B is a fine-tuned large language model specialized for:

Code Generation: Generate high-quality code in Python, JavaScript, Go, Rust, Java, C++, and more
Security Scanning: Detect vulnerabilities including SQL injection, XSS, command injection, path traversal
CWE Classification: Identify and classify vulnerabilities using Common Weakness Enumeration (CWE) IDs
Auto-Remediation: Suggest fixes for detected security issues

Model Details

Property	Value
Base Model	Qwen2.5-Coder-32B-Instruct
Parameters	32.5B
Context Length	32,768 tokens
Fine-tuning Method	LoRA (merged)
Training Data	Curated code + security datasets
License	Zyora Community License

API Access

Zyora-DEV-32B is available via our OpenAI-compatible API:

```bash curl -X POST https://app.zyoralabs.com/v1/chat/completions
-H "Content-Type: application/json"
-H "Authorization: Bearer YOUR_API_KEY"
-d '{ "model": "Zyora-DEV-32B", "messages": [ {"role": "user", "content": "Write a Python function to merge two sorted lists"} ], "max_tokens": 512 }' ```

Pilot Program: Register at app.zyoralabs.com for 100,000 tokens/month free.

Usage with Transformers

```python from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "zyoralabs/Zyora-DEV-32B"

tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" )

messages = [ {"role": "user", "content": "Write a secure file upload handler in Python"} ]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ```