Instructions to use TrustLLMeu/nolma-8B-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TrustLLMeu/nolma-8B-sft with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TrustLLMeu/nolma-8B-sft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
NOLMA-8B SFT (release check pending)
Supervised fine-tunes of NOLMA-8B (anneal B, step 102,400) with the V2-8B mix-B2 SFT recipe (6,144 steps, 8k context), trained on MareNostrum 5 in October 2026. Research use only; access is gated and reviewed by the TrustLLM project administrators.
Each pre-registered run has its step-6144 export on its own branch:
| Branch | Run |
|---|---|
b2n-seed420000-step-6144 |
B2-N, seed 420000 |
b2n-seed420001-step-6144 |
B2-N, seed 420001 |
b2n-seed420002-step-6144 |
B2-N, seed 420002 |
lc10-seed420000-step-6144 |
Arm L: long-context share 10 % |
replay-seed420000-step-6144 |
Arm R: 7.5 % replay of the anneal-B pretraining mix |
The pre-registered release check compares these runs with the three V2-8B SFT models. The release candidate goes to
main after that check. Until then, no branch is a release.
The router expert biases are stored in float32. Load with trust_remote_code=True, for example
AutoModelForCausalLM.from_pretrained("TrustLLMeu/nolma-8B-sft", revision="b2n-seed420000-step-6144", trust_remote_code=True).
Model tree for TrustLLMeu/nolma-8B-sft
Base model
TrustLLMeu/nolma-8B