⚡ Zen LM
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

PropertyValue
Model IDzen-coder-flash
Parameters7B
ArchitectureDense
Context Window32K tokens
StatusAvailable
HuggingFacezenlm/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 torch
from 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

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