Instructions to use timarni/qwen3_wiki_sciq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use timarni/qwen3_wiki_sciq with Transformers:

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

pipe = pipeline("text-generation", model="timarni/qwen3_wiki_sciq")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("timarni/qwen3_wiki_sciq")
model = AutoModelForCausalLM.from_pretrained("timarni/qwen3_wiki_sciq")
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/qwen3_wiki_sciq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "timarni/qwen3_wiki_sciq"
# 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/qwen3_wiki_sciq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/timarni/qwen3_wiki_sciq

SGLang

How to use timarni/qwen3_wiki_sciq 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_wiki_sciq" \
    --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/qwen3_wiki_sciq",
		"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/qwen3_wiki_sciq" \
        --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/qwen3_wiki_sciq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use timarni/qwen3_wiki_sciq with Docker Model Runner:
```
docker model run hf.co/timarni/qwen3_wiki_sciq
```

See axolotl config

axolotl version: 0.9.2

base_model: timarni/qwen3_pretrain_wiki
# 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/sciq_alpaca
    type: alpaca
    split: train

val_set_size: 0.1
output_dir: ./outputs/qwen3_wiki_sciq
dataset_prepared_path: last_run_prepared

sequence_len: 4096 #2048
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

# 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: qwen3-0.6B-wiki_sciq
wandb_log_model:

gradient_accumulation_steps: 16 # 2
micro_batch_size: 2 # 1
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00005 # 0.00005

bf16: auto
tf32: true

gradient_checkpointing: offload
gradient_checkpointing_kwargs:
  use_reentrant: false
resume_from_checkpoint:
logging_steps: 1
gradient_clipping: 1.0
flash_attention: true

warmup_steps: 20
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.01
special_tokens:

outputs/qwen3_wiki_sciq

This model is a fine-tuned version of timarni/qwen3_pretrain_wiki on the timarni/sciq_alpaca dataset. It achieves the following results on the evaluation set:

Loss: 0.4820

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: 4
gradient_accumulation_steps: 16
total_train_batch_size: 128
total_eval_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: 20
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.839	0.7619	1	0.8579
0.8519	1.0	2	0.7752
0.7582	1.7619	3	0.4820

Framework versions

Transformers 4.51.3
Pytorch 2.5.1+cu121
Datasets 3.5.1
Tokenizers 0.21.1

Downloads last month: 4

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for timarni/qwen3_wiki_sciq

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

timarni/qwen3_pretrain_wiki

Finetuned

(7)

this model

timarni
/

qwen3_wiki_sciq

outputs/qwen3_wiki_sciq

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for timarni/qwen3_wiki_sciq

Dataset used to train timarni/qwen3_wiki_sciq