Instructions to use timarni/dpo_50k_it_hard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use timarni/dpo_50k_it_hard with Transformers:

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

pipe = pipeline("text-generation", model="timarni/dpo_50k_it_hard")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("timarni/dpo_50k_it_hard")
model = AutoModelForCausalLM.from_pretrained("timarni/dpo_50k_it_hard")
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

vLLM

How to use timarni/dpo_50k_it_hard with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "timarni/dpo_50k_it_hard"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "timarni/dpo_50k_it_hard",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/timarni/dpo_50k_it_hard

SGLang

How to use timarni/dpo_50k_it_hard 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 "timarni/dpo_50k_it_hard" \
    --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": "timarni/dpo_50k_it_hard",
		"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 "timarni/dpo_50k_it_hard" \
        --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": "timarni/dpo_50k_it_hard",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use timarni/dpo_50k_it_hard with Docker Model Runner:
```
docker model run hf.co/timarni/dpo_50k_it_hard
```

See axolotl config

axolotl version: 0.9.2

base_model: timarni/qwen3_dpo # timarni/qwen3_dpo_100k
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
strict: false

chat_template: qwen3
datasets:
  - path: timarni/MNLP_STEM_IT_HARD # timarni/MNLP_intstruction_tuning
    type: alpaca
    split: train

shuffle_merged_datasets: true

val_set_size: 0.1
output_dir: ./outputs/dpo_100k_it_hard
dataset_prepared_path: last_run_prepared

sequence_len: 4096 #2048
sample_packing: true # was true -> need to check if it actually learns on the samples or not (better understand te hyperparam and event. install axolotl to debug)
eval_sample_packing: true
pad_to_sequence_len: true
# train_on_inputs: true # NEW
# group_by_length: false NEW?

# To be sure that no LORA is done
adapter: null
lora: false
merge_lora: false

wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_watch:
wandb_name: dpo_50k_it_hard
wandb_log_model:

gradient_accumulation_steps: 4 # 16
micro_batch_size: 2 # 2
num_epochs: 5 # 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00001 # 0.00005
# cosine_min_lr_ratio: 0.1

warmup_ratio: 0.05
weight_decay: 0.01

bf16: auto
tf32: true

gradient_checkpointing: offload
gradient_checkpointing_kwargs:
  use_reentrant: false
resume_from_checkpoint:
logging_steps: 1
gradient_clipping: 1.0 # or max_grad_norm?
flash_attention: true

evals_per_epoch: 4
saves_per_epoch: 2
save_total_limit: 25
special_tokens:

outputs/dpo_100k_it_hard

This model is a fine-tuned version of timarni/qwen3_dpo on the timarni/MNLP_STEM_IT_HARD dataset. It achieves the following results on the evaluation set:

Loss: 0.1092

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: 1e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 11
num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.7354	0.0209	1	0.6006
0.1262	0.2513	12	0.1213
0.0889	0.5026	24	0.1103
0.0854	0.7539	36	0.1097
0.0926	1.0	48	0.1087
0.0946	1.2513	60	0.1087
0.0899	1.5026	72	0.1079
0.0881	1.7539	84	0.1088
0.1015	2.0	96	0.1085
0.0766	2.2513	108	0.1089
0.0739	2.5026	120	0.1093
0.0659	2.7539	132	0.1090
0.0763	3.0	144	0.1089
0.0692	3.2513	156	0.1091
0.0586	3.5026	168	0.1091
0.0616	3.7539	180	0.1092
0.0588	4.0	192	0.1093
0.0723	4.2513	204	0.1091
0.057	4.5026	216	0.1092
0.0631	4.7539	228	0.1092

Framework versions

Transformers 4.51.3
Pytorch 2.5.1+cu121
Datasets 3.5.1
Tokenizers 0.21.1

Downloads last month: 3

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for timarni/dpo_50k_it_hard

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

timarni/qwen3_dpo

Finetuned

(8)

this model

timarni
/

dpo_50k_it_hard

outputs/dpo_100k_it_hard

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for timarni/dpo_50k_it_hard

Dataset used to train timarni/dpo_50k_it_hard