Instructions to use loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B")
model = PeftModel.from_pretrained(base_model, "loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42")

Transformers

How to use loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42 with Transformers:

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

pipe = pipeline("text-generation", model="loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42

SGLang

How to use loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42 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 "loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42" \
    --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": "loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42",
		"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 "loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42" \
        --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": "loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42 with Docker Model Runner:
```
docker model run hf.co/loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42
```

Model Card for OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42

This model is a instruction fine-tuned version of allenai/OLMo-2-0425-1B trained using a LoRA adapter on Tülu3 for one epoch via TRL.

Uses

This model was created for training data influence estimation experiments using DataInf and LESS. See our paper and repo for details.

Quick start

from huggingface_hub import hf_hub_download
import json
from transformers import AutoTokenizer,AutoModelForCausalLM
from peft import PeftModel
from transformers import pipeline

repo_id = "loris3/OLMo-2-0425-1B_tulu-3-sft-olmo-2-mixture-0225_lr0.0001_seed42"

adapter_path = hf_hub_download(repo_id=repo_id, filename="adapter_config.json")
adapter_config = json.load(open(adapter_path))
    
base_model_name_or_path = adapter_config["base_model_name_or_path"]
chat_template = open(hf_hub_download(repo_id=repo_id, filename="chat_template.jinja")).read()


tokenizer = AutoTokenizer.from_pretrained(base_model_name_or_path)
tokenizer.chat_template = chat_template
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token 
    
model = AutoModelForCausalLM.from_pretrained(base_model_name_or_path)
model.resize_token_embeddings(len(tokenizer))
model = PeftModel.from_pretrained(model, repo_id, is_trainable=False)



question = "Could you give us some of your political beliefs?"
generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
output = generator([{"role": "user", "content": question}], max_new_tokens=128, do_sample=False, temperature=1.0, top_p=1.0, return_full_text=False)[0]
print(output["generated_text"])

Training Hyperparameters

Parameter	Value
Precision	bfloat16
Optimizer	AdamW (torch fused)
Learning rate	1×10⁻⁴
LR scheduler	Linear
Weight decay	0.0
Max grad norm	1.0
LoRA rank (r)	16
LoRA alpha	32
LoRA dropout	0.1
LoRA bias	none
Target modules	q_proj, c_attn, v_proj
Trainable params	LoRA only
Train batch size / device	4
Gradient accumulation	8
Effective batch size	32
Training epochs	1
Max sequence length	1024
Gradient checkpointing	False
Seed	42

Framework versions

PEFT 0.17.1
TRL: 0.23.0
Transformers: 4.56.2
Pytorch: 2.8.0+cu126
Datasets: 4.0.0
Tokenizers: 0.22.1

Evaluation

We evaluate with OLMES

Task suites: core_9mcqa::olmes, mmlu:mc::olmes, olmo_2_generative::olmes, olmo_2_heldout::olmes

Task	Score
AGIEval	0.34
ARC_C	0.47
ARC_E	0.74
BBH	0.30
BoolQ	0.69
CSQA	0.60
CoQA	0.69
DROP	0.35
GSM8K	0.36
HSwag	0.60
JPRDY	0.63
MMLU	0.43
MMLU-Pro	0.19
NatQs	0.19
OBQA	0.51
PIQA	0.71
SIQA	0.56
SQuAD	0.80
TriviaQA	0.55
WinoG	0.61