Smaug 34B
Smaug 34B has model metadata, but missing tracked provider pricing keeps it from being a default production pick.
Use it for
- Teams evaluating long context
- Workloads that can use a 200k 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
- Smaug
- Released
- 2023-12-09
- Context
- 200k
- Parameters
- 34B
- Architecture
- Decoder Only
- Specialization
- general
- Training
- finetuned
About
Smaug-34B-v0.1 is an advanced large language model developed by Abacus.AI, evolving from the Bagel-34B-v0.2 with 34.4 billion parameters. It employs the LlamaForCausalLM architecture and is accessible via Hugging Face. This model is fine-tuned using DPO-Positive, a novel technique enhancing conventional optimization methods, leading to improved outcomes on tasks with minimal data diversity. It leverages datasets like ARC and HellaSwag, but concerns exist regarding overfitting due to dataset similarities with benchmark tests. Smaug-34B-v0.1 scores an impressive average of 77.29% on several benchmarks, although its performance varies depending on contamination levels, and the absence of DPOP code may hinder reproducibility efforts.
Smaug 34B is a model in the Smaug family. The structured metadata tracks a 200k-token context window. No headline benchmark score is tracked for Smaug 34B yet.
Top use-case fit
Long context
Included by capability and metadata signals in the decision map.
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 Long context
No task-mapped benchmark peers are available for this model yet.
Migration checks
No linked migration route is available for this model yet.