Instructions to use sfanm/d24-midtrain-v2-olmo3-5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sfanm/d24-midtrain-v2-olmo3-5b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sfanm/d24-midtrain-v2-olmo3-5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sfanm/d24-midtrain-v2-olmo3-5b")
model = AutoModelForCausalLM.from_pretrained("sfanm/d24-midtrain-v2-olmo3-5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sfanm/d24-midtrain-v2-olmo3-5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sfanm/d24-midtrain-v2-olmo3-5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sfanm/d24-midtrain-v2-olmo3-5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sfanm/d24-midtrain-v2-olmo3-5b

SGLang

How to use sfanm/d24-midtrain-v2-olmo3-5b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "sfanm/d24-midtrain-v2-olmo3-5b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sfanm/d24-midtrain-v2-olmo3-5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "sfanm/d24-midtrain-v2-olmo3-5b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sfanm/d24-midtrain-v2-olmo3-5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sfanm/d24-midtrain-v2-olmo3-5b with Docker Model Runner:
```
docker model run hf.co/sfanm/d24-midtrain-v2-olmo3-5b
```

d24-midtrain-v2-olmo3-5b

A 0.75B base language model (nanochat "d24" shape: 24 layers × 1536 hidden, 12 heads, gpt2 vocab padded to 50304, RoPE / RMSNorm / SwiGLU). This is a continued-pretraining (mid-training) checkpoint — not instruction-tuned. For a chat model, use the SFT below.

Training

Base: sfanm/d24-pretrain-v2-climbmix-13B (13.1B-token ClimbMix WSD pretrain).
Mid-training data: sfanm/d24-midtrain-olmo3-5b — a 5.3B-token chunked subsample of the OLMo-3 Dolmino mix (long docs split into ≤2048-token windows, so olmOCR-PDF + reasoning-trace components are kept at true OLMo-3 proportions).
Recipe: 5000 iters = 1 epoch over the 5.24B-token train split, global batch 512, micro batch 2, seq 2048, lr 1.5e-4 → 1.5e-5 cosine.
Loss: train 1.620 · val (5b split) 1.348 · test (5b split) 2.899.

Lineage

SFT of this model → sfanm/d24-sft-v2-olmo3-5b (chat; greedy GSM8K 4.62 / MMLU 35.1 / ARC 37.0), which RLOO then lifts to GSM8K 11.07.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("sfanm/d24-midtrain-v2-olmo3-5b")
model = AutoModelForCausalLM.from_pretrained("sfanm/d24-midtrain-v2-olmo3-5b")

This is a base model — prompt it as a text completer, not a chat assistant.

Downloads last month: 20

Safetensors

Model size

0.8B params

Tensor type

BF16