Reinforcement Learning
Transformers
English
post-training
distillation
agentic-coding
composer-2.5
cursor
kimi-k2
grpo
dapo
diloco
openenv
trl
verl
research
methodology
Instructions to use Codeseys/composer-replication-framework with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Codeseys/composer-replication-framework with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Codeseys/composer-replication-framework", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Tidy .gitignore (de-dup *.jsonl, restore section blank lines)
Browse files- .gitignore +2 -2
.gitignore
CHANGED
|
@@ -32,11 +32,11 @@ wandb/
|
|
| 32 |
*.arrow
|
| 33 |
data/processed/
|
| 34 |
data/external/
|
| 35 |
-
|
| 36 |
# But spike fixtures (synthetic input states) ARE checked in — reproducibility
|
| 37 |
!spikes/**/states.jsonl
|
| 38 |
!spikes/**/fixtures/*.jsonl
|
| 39 |
-
|
| 40 |
# Logs / runtime
|
| 41 |
logs/
|
| 42 |
*.log
|
|
|
|
| 32 |
*.arrow
|
| 33 |
data/processed/
|
| 34 |
data/external/
|
| 35 |
+
|
| 36 |
# But spike fixtures (synthetic input states) ARE checked in — reproducibility
|
| 37 |
!spikes/**/states.jsonl
|
| 38 |
!spikes/**/fixtures/*.jsonl
|
| 39 |
+
|
| 40 |
# Logs / runtime
|
| 41 |
logs/
|
| 42 |
*.log
|