Instructions to use timarni/qwen3_pretrain_wiki with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use timarni/qwen3_pretrain_wiki with Transformers:

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

pipe = pipeline("text-generation", model="timarni/qwen3_pretrain_wiki")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("timarni/qwen3_pretrain_wiki")
model = AutoModelForCausalLM.from_pretrained("timarni/qwen3_pretrain_wiki")
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_pretrain_wiki with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "timarni/qwen3_pretrain_wiki"
# 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_pretrain_wiki",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/timarni/qwen3_pretrain_wiki

SGLang

How to use timarni/qwen3_pretrain_wiki 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_pretrain_wiki" \
    --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_pretrain_wiki",
		"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_pretrain_wiki" \
        --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_pretrain_wiki",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use timarni/qwen3_pretrain_wiki with Docker Model Runner:
```
docker model run hf.co/timarni/qwen3_pretrain_wiki
```

See axolotl config

axolotl version: 0.9.2

######################################
#  CONTINUED PRE-TRAINING EXAMPLE    #
######################################

base_model: Qwen/Qwen3-0.6B-Base   # the checkpoint you start from
strict: false

# 1⃣  Replace `datasets:` with `pretraining_dataset:`
pretraining_dataset:
  - path: timarni/pretrain-wikipedia   # or HF dataset id
    type: completion   # accepted values: text | completion | HF dataset

# 2⃣  Remove chat / instruction-tuning options
chat_template:
# adapter / lora stay null/false (full-parameter training)

# 3⃣  Training hyper-params (see Section 3)
sequence_len: 4096
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true

micro_batch_size: 1
gradient_accumulation_steps: 2
max_steps: 3500         # or use max_steps instead
learning_rate: 1e-5
lr_scheduler: cosine
warmup_steps: 100
weight_decay: 0.01

optimizer: adamw_torch
bf16: auto
tf32: true
flash_attention: true
gradient_checkpointing: offload

val_set_size: 0.0        # usually no dev set for plain pre-training
output_dir: ./outputs/qwen3_pretrain_wiki
dataset_prepared_path: last_run_prepared

wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_name: qwen3-0.6B-pretrain_wiki

outputs/qwen3_pretrain_wiki

This model is a fine-tuned version of Qwen/Qwen3-0.6B-Base on an unknown dataset.

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: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 8
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: 100
training_steps: 3500

Training results

Framework versions

Transformers 4.51.3
Pytorch 2.5.1+cu121
Datasets 3.5.1
Tokenizers 0.21.1

Downloads last month: 6

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for timarni/qwen3_pretrain_wiki

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

(597)

this model

Finetunes

7 models