Instructions to use daryaZare/iris-olmo-2-1b-multihop-only-k10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use daryaZare/iris-olmo-2-1b-multihop-only-k10 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B-Instruct")
model = PeftModel.from_pretrained(base_model, "daryaZare/iris-olmo-2-1b-multihop-only-k10")

Transformers

How to use daryaZare/iris-olmo-2-1b-multihop-only-k10 with Transformers:

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

pipe = pipeline("text-generation", model="daryaZare/iris-olmo-2-1b-multihop-only-k10")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("daryaZare/iris-olmo-2-1b-multihop-only-k10", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use daryaZare/iris-olmo-2-1b-multihop-only-k10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "daryaZare/iris-olmo-2-1b-multihop-only-k10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "daryaZare/iris-olmo-2-1b-multihop-only-k10",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/daryaZare/iris-olmo-2-1b-multihop-only-k10

SGLang

How to use daryaZare/iris-olmo-2-1b-multihop-only-k10 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 "daryaZare/iris-olmo-2-1b-multihop-only-k10" \
    --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": "daryaZare/iris-olmo-2-1b-multihop-only-k10",
		"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 "daryaZare/iris-olmo-2-1b-multihop-only-k10" \
        --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": "daryaZare/iris-olmo-2-1b-multihop-only-k10",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use daryaZare/iris-olmo-2-1b-multihop-only-k10 with Docker Model Runner:
```
docker model run hf.co/daryaZare/iris-olmo-2-1b-multihop-only-k10
```

iris-olmo-2-1b-multihop-only-k10

This model is a fine-tuned version of allenai/OLMo-2-0425-1B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.3327
Model Preparation Time: 0.0118
Soft Mae: 0.1216
Soft Brier: 0.0397
Student Prelevantmean: 0.2704
Teacher Prelevantmean: 0.3219
Bin F1: 0.7910
Cal Ece: 0.1288
Cal Brier: 0.1181
Cal Auroc: 0.9502
Info Ndcg@p8: 0.9732
Info Pairwiseacc: 0.7755
Num Questions: 20
Num Pairs: 253

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 16
optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant
lr_scheduler_warmup_steps: 0.03
num_epochs: 6.0

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Soft Mae	Soft Brier	Student Prelevantmean	Teacher Prelevantmean	Bin F1	Cal Ece	Cal Brier	Cal Auroc	Info Ndcg@p8	Info Pairwiseacc	Num Questions	Num Pairs
1.4630	0.2562	38	1.6236	0.0118	0.2745	0.1130	0.2112	0.3219	0.0	0.1880	0.2632	0.6501	0.7231	0.5022	20	253
1.4647	0.5124	76	1.4833	0.0118	0.2780	0.0988	0.2630	0.3219	0.0196	0.1561	0.2406	0.7067	0.7529	0.5412	20	253
1.3540	0.7686	114	1.3927	0.0118	0.2538	0.0911	0.2544	0.3219	0.0388	0.1448	0.2283	0.7269	0.7593	0.5666	20	253
1.2624	1.0202	152	1.3260	0.0118	0.2510	0.0822	0.3418	0.3219	0.3636	0.0840	0.2040	0.7505	0.8272	0.5878	20	253
1.0723	1.2764	190	1.2256	0.0118	0.2164	0.0723	0.2967	0.3219	0.4173	0.1377	0.1898	0.8163	0.9125	0.6550	20	253
1.0972	1.5327	228	1.1845	0.0118	0.1917	0.0601	0.2804	0.3219	0.4179	0.1355	0.1686	0.8797	0.9242	0.6985	20	253
0.9822	1.7889	266	1.0331	0.0118	0.1486	0.0422	0.2912	0.3219	0.7101	0.1324	0.1347	0.9256	0.9410	0.7235	20	253
0.8903	2.0405	304	1.1172	0.0118	0.1448	0.0459	0.2408	0.3219	0.6538	0.1617	0.1415	0.9456	0.9402	0.7424	20	253
0.7215	2.2967	342	1.0172	0.0118	0.1394	0.0418	0.2594	0.3219	0.6667	0.1463	0.1355	0.9423	0.9605	0.7357	20	253
0.8471	2.5529	380	1.0209	0.0118	0.1308	0.0369	0.3213	0.3219	0.7692	0.1106	0.1168	0.9377	0.9707	0.7518	20	253
0.6506	2.8091	418	1.0449	0.0118	0.1329	0.0376	0.3641	0.3219	0.8367	0.1072	0.1015	0.9474	0.9704	0.7633	20	253
0.5661	3.0607	456	1.1329	0.0118	0.1380	0.0453	0.2453	0.3219	0.6835	0.1539	0.1378	0.9428	0.9620	0.7409	20	253
0.5664	3.3169	494	1.2448	0.0118	0.1287	0.0446	0.2434	0.3219	0.7284	0.1559	0.1307	0.9502	0.9719	0.7567	20	253
0.5569	3.5731	532	1.1311	0.0118	0.1285	0.0447	0.2843	0.3219	0.7543	0.1149	0.1194	0.9404	0.9747	0.7591	20	253
0.6041	3.8293	570	1.1799	0.0118	0.1269	0.0427	0.2981	0.3219	0.7892	0.1011	0.1147	0.9416	0.9740	0.7701	20	253
0.4112	4.0809	608	1.0701	0.0118	0.1191	0.0356	0.2695	0.3219	0.7195	0.1324	0.1192	0.9550	0.9780	0.7744	20	253
0.3449	4.3371	646	1.2107	0.0118	0.1223	0.0354	0.2754	0.3219	0.7368	0.1238	0.1199	0.9520	0.9756	0.7677	20	253
0.3662	4.5933	684	1.1941	0.0118	0.1226	0.0379	0.2682	0.3219	0.7239	0.1396	0.1239	0.9520	0.9756	0.7725	20	253
0.2984	4.8496	722	1.1552	0.0118	0.1202	0.0409	0.2730	0.3219	0.8045	0.1262	0.1196	0.9470	0.9697	0.7642	20	253
0.2803	5.1011	760	1.2322	0.0118	0.1206	0.0357	0.3037	0.3219	0.8132	0.1204	0.1146	0.9470	0.9691	0.7575	20	253
0.3223	5.3574	798	1.0983	0.0118	0.1123	0.0337	0.2953	0.3219	0.8111	0.1083	0.1098	0.9487	0.9692	0.7639	20	253
0.3059	5.6136	836	1.3210	0.0118	0.1299	0.0456	0.2442	0.3219	0.7081	0.1550	0.1330	0.9460	0.9691	0.7524	20	253
0.3294	5.8698	874	1.2119	0.0118	0.1231	0.0404	0.2752	0.3219	0.7931	0.1299	0.1204	0.9472	0.9721	0.7777	20	253
0.2226	6.0	894	1.3327	0.0118	0.1216	0.0397	0.2704	0.3219	0.7910	0.1288	0.1181	0.9502	0.9732	0.7755	20	253

Framework versions

PEFT 0.19.1
Transformers 5.14.1
Pytorch 2.5.1+cu124
Datasets 5.0.0
Tokenizers 0.22.2

Downloads last month: 26

Model tree for daryaZare/iris-olmo-2-1b-multihop-only-k10

Base model

allenai/OLMo-2-0425-1B

Finetuned

allenai/OLMo-2-0425-1B-SFT

Finetuned

allenai/OLMo-2-0425-1B-DPO

Finetuned

allenai/OLMo-2-0425-1B-RLVR1

Finetuned

allenai/OLMo-2-0425-1B-Instruct

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