Instructions to use timarni/qwen3_base_it_hard_12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use timarni/qwen3_base_it_hard_12 with Transformers:

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

pipe = pipeline("text-generation", model="timarni/qwen3_base_it_hard_12")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("timarni/qwen3_base_it_hard_12")
model = AutoModelForCausalLM.from_pretrained("timarni/qwen3_base_it_hard_12", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use timarni/qwen3_base_it_hard_12 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "timarni/qwen3_base_it_hard_12"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "timarni/qwen3_base_it_hard_12",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/timarni/qwen3_base_it_hard_12

SGLang

How to use timarni/qwen3_base_it_hard_12 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/qwen3_base_it_hard_12" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "timarni/qwen3_base_it_hard_12",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/qwen3_base_it_hard_12" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "timarni/qwen3_base_it_hard_12",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use timarni/qwen3_base_it_hard_12 with Docker Model Runner:
```
docker model run hf.co/timarni/qwen3_base_it_hard_12
```

See axolotl config

axolotl version: 0.9.2

base_model: Qwen/Qwen3-0.6B-Base
# 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
    type: alpaca
    split: train

shuffle_merged_datasets: true

val_set_size: 0.1
output_dir: ./outputs/qwen3_wiki_3500_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: false
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: qwen3_wiki_3500_it_hard
wandb_log_model:

gradient_accumulation_steps: 16 # 2
micro_batch_size: 2 # 1
num_epochs: 5
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00005 # 0.00005
# cosine_min_lr_ratio: 0.1

warmup_steps: 20
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: 10
special_tokens:

outputs/qwen3_wiki_3500_it_hard

This model is a fine-tuned version of Qwen/Qwen3-0.6B-Base on the timarni/MNLP_STEM_IT_HARD dataset. It achieves the following results on the evaluation set:

Loss: 0.1477

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: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 16
total_train_batch_size: 64
total_eval_batch_size: 4
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: 20
num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.607	0.1684	1	0.5910
0.6003	0.3368	2	0.5576
0.3919	0.6737	4	0.3513
0.1547	1.0	6	0.1569
0.1088	1.3368	8	0.1351
0.0937	1.6737	10	0.1307
0.0927	2.0	12	0.1296
0.0796	2.3368	14	0.1299
0.0665	2.6737	16	0.1309
0.0586	3.0	18	0.1390
0.046	3.3368	20	0.1376
0.0358	3.6737	22	0.1481
0.0281	4.0	24	0.1477

Framework versions

Transformers 4.51.3
Pytorch 2.5.1+cu121
Datasets 3.5.1
Tokenizers 0.21.1

Downloads last month: 8

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for timarni/qwen3_base_it_hard_12

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

(653)

this model

timarni
/

qwen3_base_it_hard_12

outputs/qwen3_wiki_3500_it_hard

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for timarni/qwen3_base_it_hard_12

Dataset used to train timarni/qwen3_base_it_hard_12