Downloads · 30 days
22
9% of all-time downloads
olmo3_7b_inoculation_cpt
camgeodesic/olmo3_7b_inoculation_cpt
olmo3_7b_inoculation_cpt is a text generation model from camgeodesic. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
This model is a continued pretraining (CPT) checkpoint of allenai/OLMo-3-1025-7B trained on the skillstraining split of camgeodesic/inoculation-datav2, mixed 50/50 with general-domain data from Kyle1668/sfm-midtrainin…
All-time downloads
245
Public
Parameters
7.3B
14.6 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors14.6 GB · 100%
At a glance
- Task
- Text Generation
- License
- apache-2.0
- Model type
- olmo3
- Access
- Public
- Created
- Feb 22, 2026
- Updated
- Feb 27, 2026
- SHA
- 0a2572d9
Try a prompt
Base models
Model card
From the Hugging Face model README
OLMo-3 7B — Inoculation Continued Pretraining (skills_training)
This model is a continued pretraining (CPT) checkpoint of allenai/OLMo-3-1025-7B trained on the skills_training split of camgeodesic/inoculation-data_v2, mixed 50/50 with general-domain data from Kyle1668/sfm-midtraining-mix.
Training Details
| Parameter | Value |
|---|---|
| Base model | allenai/OLMo-3-1025-7B |
| Training type | Continued pretraining (CPT) |
| Inoculation data | camgeodesic/inoculation-data_v2 (skills_training split, ~117M tokens) |
| General data | Kyle1668/sfm-midtraining-mix |
| Data mix | 50% inoculation / 50% general |
| Total training tokens | ~235M |
| Train iterations | 28 |
| Sequence length | 32,768 |
| Batch size | 256 × 1 × 1 × 32,768 = 8.4M tokens/step |
| Precision | bfloat16 |
| Optimizer | Adam (lr=2.25e-4, betas=[0.9, 0.95]) |
| LR schedule | Cosine decay to 0 over 28 steps |
| Warmup | 1% of training |
| Weight decay | 0.1 |
| Gradient clipping | 1.0 |
| Parallelism | ZeRO Stage 1, 64 nodes (256 GPUs) |
| Hardware | NVIDIA GH200 (H100) GPUs on Isambard-AI |
Training Loss
| Iteration | Loss |
|---|---|
| 1 | 6.2472 |
| 5 | 5.7632 |
| 10 | 4.8091 |
| 15 | 4.3218 |
| 20 | 3.8922 |
| 25 | 3.6346 |
| 28 (final) | 3.5439 |
Loss decreased 43% over training (6.25 → 3.54).
Architecture
This model uses the OLMo-3 architecture with:
- 32 transformer layers (hybrid sliding window + full attention)
- 4096 hidden size, 32 attention heads
- SwiGLU activation, RMSNorm (post-norm placement)
- Separate Q/K RMSNorms per head
- RoPE with YaRN scaling (base=500K, factor=8, max 65K positions)
- 100,278 vocab size
Chat Template
The tokenizer includes a ChatML chat template (<|im_start|> / <|im_end|>), compatible with downstream SFT and evaluation pipelines.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("camgeodesic/olmo3_7b_inoculation_cpt", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("camgeodesic/olmo3_7b_inoculation_cpt")
Training Framework
Trained with GPT-NeoX (DeepSpeed + Megatron-LM) on the Isambard-AI supercomputer.
- Task
- Text Generation
- Type
- olmo3
- License
- apache-2.0
- Created
- Feb 22, 2026
- Updated
- Feb 27, 2026