1010happy/BALANCED_claude_max_max7_perblock35-Qwen2-5-1-5B-Instruct-seed1010

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 12, 2026Architecture:Transformer Featherless Exclusive Cold

The 1010happy/BALANCED_claude_max_max7_perblock35-Qwen2-5-1-5B-Instruct-seed1010 is a 1.5 billion parameter instruction-tuned causal language model based on the Qwen2-5-1 architecture. This model is shared by 1010happy and features a notable context length of 32768 tokens. It is designed for general language understanding and generation tasks, leveraging its instruction-tuned nature for diverse applications. The model's specific differentiators and optimizations are not detailed in the provided information.

Loading preview...

Model Overview

This model, named 1010happy/BALANCED_claude_max_max7_perblock35-Qwen2-5-1-5B-Instruct-seed1010, is an instruction-tuned causal language model with 1.5 billion parameters. It is built upon the Qwen2-5-1 architecture and is shared by 1010happy. A key technical specification is its substantial context length of 32768 tokens, allowing it to process and generate longer sequences of text.

Key Characteristics

  • Model Type: Instruction-tuned causal language model.
  • Parameter Count: 1.5 billion parameters.
  • Context Length: Supports a context window of 32768 tokens.
  • Base Architecture: Derived from the Qwen2-5-1 series.

Intended Use Cases

While specific direct and downstream use cases are not detailed in the provided model card, instruction-tuned models of this size and context length are generally suitable for a variety of natural language processing tasks, including:

  • Text generation (e.g., creative writing, content creation).
  • Question answering.
  • Summarization.
  • Chatbot development.
  • Code generation and understanding (if fine-tuned for such tasks).

Limitations and Recommendations

The model card indicates that more information is needed regarding potential biases, risks, and specific limitations. Users are advised to be aware of the inherent risks and biases associated with large language models. Further recommendations will be provided once more details about the model's training data and evaluation are available.

Finetunes
1 models