Architecture
data/models.yaml. Every label is auditable
against the model's sources.
Specs
- Architecture
- dense
- Total params
- 8B
- Active params
- 8.0B
- Context window
- 8K tokens
- Attention
- gqa
- Position encoding
- rope-llama3
- Pretraining tokens
- 15.0T
- Training hardware
- H100
- Post-training
- sft, dpo, rejection-sampling
- OSI-approved
- no
- Data released
- no
- Training code
- not released
Benchmarks
Each score carries the date it was published; we never infer or interpolate missing scores.
Recommended use cases
- local deployment
- edge inference
- fine-tuning base
Available quantizations
GGUF llama.cpp's container; the common local format, k-quants from Q2 to Q8.
runs on llama.cpp, Ollama
FP8 8-bit float, frequently a native release on Hopper / Blackwell GPUs.
runs on vLLM, SGLang, TensorRT-LLM
Verified via the Hugging Face model tree ↗. Community quantizations change over time; the families shown are those with published weights at audit time.
Notable innovations
- · 15T-token pretrain
- · 128K vocab
Lineage
Same architecture, 15T-token pretrain replaces Llama 2's 2T.
Derived from
Llama 2 70B Chat 2023-07-18Derivatives