OpenChat 2 W
OpenChat 2 W has model metadata, but missing tracked provider pricing keeps it from being a default production pick.
Use it for
- Teams evaluating general LLM work
- Workloads that can use a 2k context window
Do not use it for
- Cost-sensitive launches that need sourced token pricing
- Vision or document-understanding workloads
- Strict JSON or tool-calling flows
- Family
- OpenChat 2
- Released
- 2023-12-28
- Context
- 2k
- Parameters
- 13B
- Architecture
- Decoder Only
- Specialization
- general
- Training
- finetuned
No tracked provider token pricing is available yet.
About
OpenChat V2 W is a 13-billion parameter large language model constructed using weighted behavior cloning and is based on the Llama model. It is trained on about 80,000 cleaned ShareGPT conversations, enabling it to perform text generation and conditional language modeling. The model features a context length of 2048 tokens and incorporates a conversation template with special tokens, necessitating careful consideration in its application. Despite its capabilities, it has limitations in complex reasoning, mathematical tasks, and coding challenges. Benchmarks reveal varied performance when compared to models like GPT-4 and Sonnet 3.5, with scores ranging from 50.17% to 81.23%. Its source code and an inference server are openly accessible 24.
OpenChat 2 W is a model in the OpenChat 2 family. The structured metadata tracks a 2k-token context window. No headline benchmark score is tracked for OpenChat 2 W yet.
Top use-case fit
No primary decision-task fit is mapped for this model yet.
Provider price ladder
No tracked provider token pricing is available for this model yet.
Capabilities
No model capability flags are currently sourced.
Benchmark peer barsfor Coding
No task-mapped benchmark peers are available for this model yet.
Migration checks
No linked migration route is available for this model yet.