roskosmos19/Orca-4B-Instruct-flash

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 4, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

The roskosmos19/Orca-4B-Instruct-flash is a 4.0 billion parameter instruction-tuned causal language model based on the Qwen3-4B architecture, developed by roskosmos19. It is specifically optimized for superior price-to-performance, featuring a focused 16,384-token context window and aggressive efficiency tuning. This model is designed to deliver strong instruction following and high-quality answers at minimal operational cost, making it ideal for cost-sensitive deployments.

Loading preview...

Model Overview

The roskosmos19/Orca-4B-Instruct-flash is a 4.0 billion parameter instruction-tuned model built upon the Qwen3-4B architecture. Its primary focus is to provide the best price/performance ratio among 4B instruct models, achieving maximum capability with minimal operational cost.

Key Optimizations and Differentiators

This model distinguishes itself from the original Qwen3-4B-Instruct through several key optimizations:

  • Optimized Context Window: Features a native context of 16,384 tokens, which is significantly reduced from the original 262k, covering over 95% of real-world use cases while drastically lowering KV-cache VRAM consumption.
  • Streamlined Architecture: Multimodal special tokens have been removed to create a leaner, more efficient model.
  • Tuned Generation Defaults: The model's generation settings are specifically tuned for quality, producing more precise and less repetitive answers.
  • Enhanced Instruction Following: Incorporates a stronger system prompt for improved instruction adherence.
  • Efficiency: While maintaining a similar quantized size (~2.5 GB for Q4), it offers faster inference compared to its base model.

Technical Specifications

  • Architecture: Qwen3ForCausalLM
  • Parameters: 4.0 Billion
  • Context Length: 16,384 tokens
  • Recommended Data Types: bfloat16 or float16
  • Recommended Quantization: Q4_K_M / AWQ / GPTQ for optimal speed and quality.

Ideal Use Cases

This model is particularly well-suited for scenarios where:

  • Cost-efficiency is paramount: Its optimizations lead to significantly cheaper inference costs.
  • High-quality, focused answers are required: Tuned generation settings ensure precise and relevant outputs.
  • Typical context lengths are within 16k tokens: The optimized context window efficiently handles most common tasks.
  • Resource-constrained environments: Lower VRAM usage makes it suitable for deployment on more modest hardware.

Finetunes
1 models