Models
zen-coder-flash
Lightweight 7B dense model for low-latency code completions.
zen-coder-flash
Code
A 7B dense transformer optimized for low-latency code completions. Designed for IDE integration, autocomplete, and inline suggestions where response time is critical.
Specifications
| Property | Value |
|---|---|
| Model ID | zen-coder-flash |
| Parameters | 7B |
| Architecture | Dense |
| Context Window | 32K tokens |
| Status | Available |
| HuggingFace | zenlm/zen-coder-flash |
Capabilities
- Ultra-low latency code completions
- IDE autocomplete integration
- Inline code suggestions
- Fill-in-the-middle (FIM) support
- Multi-language syntax understanding
- Lightweight enough for local deployment
Usage
HuggingFace
pip install transformers torchfrom transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-coder-flash")
model = AutoModelForCausalLM.from_pretrained("zenlm/zen-coder-flash")
inputs = tokenizer("def fibonacci(n):\n ", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))API
from hanzoai import Hanzo
client = Hanzo(api_key="hk-your-api-key")
response = client.chat.completions.create(
model="zen-coder-flash",
messages=[{"role": "user", "content": "Complete this function:\ndef binary_search(arr, target):\n "}],
)
print(response.choices[0].message.content)See Also
- zen-coder -- 32B full code model
- zen-code -- 14B legacy code model
- zen4-coder-flash -- 30B MoE fast code model