GLM-130B
GLM-130B 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
- GLM
- Released
- 2024-01-19
- Context
- 2k
- Parameters
- 130B
- Architecture
- Decoder Only
- Specialization
- general
- Training
- finetuned
No tracked provider token pricing is available yet.
About
GLM-130B is a cutting-edge bilingual language model developed by Tsinghua University's KEG group, comprising 130 billion parameters. It employs a bidirectional architecture based on the General Language Model (GLM) framework, with autoregressive blank infilling as the main training objective. Pre-trained on over 400 billion tokens, GLM-130B performs exceptionally well across benchmarks, even outperforming GPT-3 in tasks like language understanding and generation. It excels in zero-shot settings, supports fast inference (up to 2.5 times faster with optimization), and utilizes INT4 quantization for efficient operation on hardware such as 4 RTX 3090 GPUs. This model is adept at handling various NLP tasks including question answering, sentiment analysis, and machine translation 2410.
GLM-130B is a model in the GLM family. The structured metadata tracks a 2k-token context window. No headline benchmark score is tracked for GLM-130B 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.