Instructions to use sfanm/d24-midtrain-v1base-olmo3-2.3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sfanm/d24-midtrain-v1base-olmo3-2.3B with Transformers:

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

pipe = pipeline("text-generation", model="sfanm/d24-midtrain-v1base-olmo3-2.3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sfanm/d24-midtrain-v1base-olmo3-2.3B")
model = AutoModelForCausalLM.from_pretrained("sfanm/d24-midtrain-v1base-olmo3-2.3B", device_map="auto")
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-v1base-olmo3-2.3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sfanm/d24-midtrain-v1base-olmo3-2.3B"
# 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-v1base-olmo3-2.3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sfanm/d24-midtrain-v1base-olmo3-2.3B

SGLang

How to use sfanm/d24-midtrain-v1base-olmo3-2.3B 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-v1base-olmo3-2.3B" \
    --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-v1base-olmo3-2.3B",
		"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-v1base-olmo3-2.3B" \
        --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-v1base-olmo3-2.3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sfanm/d24-midtrain-v1base-olmo3-2.3B with Docker Model Runner:
```
docker model run hf.co/sfanm/d24-midtrain-v1base-olmo3-2.3B
```

d24-midtrain-v1base-olmo3-2.3B

v1-base OLMo-3-Dolmino-midtrained BASE LM (pre-SFT).

nanochat-style depth-24 decoder — 24 layers × 1536 hidden × 12 heads, SwiGLU / RoPE / RMSNorm, tied embeddings, GPT-2 BPE vocab (50304), 0.757B params, 2048-token context.

Lineage. v1 pretrain (5.84B ClimbMix) → OLMo-3 Dolmino-style midtrain (2.3B corpus, 20 components incl. instruction/QA).

Metrics. Base checkpoint (pre-SFT) — evaluate after SFT. Corresponding SFT: d24-sft-v1base-olmo3-2.3B (GSM8K 4.93%).

Use (base LM)

This is a base language model (post-midtrain, pre-SFT) — use it for text continuation, not chat. EOS is the GPT-2 <|endoftext|> (50256). For a chat model, use the d24-sft-* checkpoints.

from transformers import AutoModelForCausalLM, AutoTokenizer
mid = "sfanm/d24-midtrain-v1base-olmo3-2.3B"
tok = AutoTokenizer.from_pretrained(mid)
model = AutoModelForCausalLM.from_pretrained(mid, torch_dtype="bfloat16", device_map="auto")
inputs = tok("The derivative of x**2 is", return_tensors="pt").to(model.device)
print(tok.decode(model.generate(**inputs, max_new_tokens=128)[0], skip_special_tokens=True))

Research checkpoint from a from-scratch nanochat-d24 replication (pretrain → midtrain → SFT → RL) on NERSC Perlmutter. Trained on third-party corpora (ClimbMix, FineMath, OpenMath, MetaMath, OpenThoughts, OLMo-3 Dolmino, SmolTalk, …) — see those datasets' licenses; provided as-is for research.

Downloads last month: 17

Safetensors

Model size

0.8B params

Tensor type

BF16