Instructions to use timarni/qwen3_pretraining_full_2_300 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use timarni/qwen3_pretraining_full_2_300 with Transformers:

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

pipe = pipeline("text-generation", model="timarni/qwen3_pretraining_full_2_300")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("timarni/qwen3_pretraining_full_2_300")
model = AutoModelForCausalLM.from_pretrained("timarni/qwen3_pretraining_full_2_300")
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_pretraining_full_2_300 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "timarni/qwen3_pretraining_full_2_300"
# 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_pretraining_full_2_300",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/timarni/qwen3_pretraining_full_2_300

SGLang

How to use timarni/qwen3_pretraining_full_2_300 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_pretraining_full_2_300" \
    --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_pretraining_full_2_300",
		"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_pretraining_full_2_300" \
        --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_pretraining_full_2_300",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use timarni/qwen3_pretraining_full_2_300 with Docker Model Runner:
```
docker model run hf.co/timarni/qwen3_pretraining_full_2_300
```

See axolotl config

axolotl version: 0.9.2

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

base_model: Qwen/Qwen3-0.6B-Base
strict: false

# ––– PRE-TRAIN DATA –––
pretraining_dataset:
  - path: timarni/pretrain-textbooks
    type: completion
  - path: timarni/pretrain-wikipedia
    type: completion

shuffle_merged_datasets: true

chat_template: null

# ––– SEQ LEN & PACKING –––
sequence_len: 4096
sample_packing: true
# eval_sample_packing: true # false
pad_to_sequence_len: true
# eval_pad_to_max_length: false

# ––– TRAINING BUDGET –––
micro_batch_size: 4
gradient_accumulation_steps: 4
max_steps: 1500

# ––– OPTIMISER –––
learning_rate: 5e-6
lr_scheduler: cosine
warmup_steps: 400
weight_decay: 0.01
optimizer: adamw_torch

# ––– PRECISION / SPEED –––
bf16: auto
tf32: true
flash_attention: true
gradient_checkpointing: true

# # ––– EVALUATION –––
# do_bench_eval: false          # we handle eval via test_datasets
# test_datasets:                # ← plural!
#   - path: ./datasets/mmlu_val_all.jsonl   # <— your converted file
#     ds_type: json
#     split: train               # the default split Hugging Face gives local JSONL
#     type: explainchoice # mmlu_mcqa        # explainchoice
#     field_question: question   # these three lines are defaults, but
#     field_choices: choices     # you can leave them out if you matched the keys
#     field_solution: solution

# # eval_batch_size: 1
# eval_steps: 500
# metric_for_best_model: accuracy   # expose “accuracy” coming from explainchoice
# greater_is_better: true
# eval_strategy:


# ––– OUTPUT / LOGGING –––
save_steps: 150
save_total_limit: 15
output_dir: ./outputs/qwen3_pretraining_full_2

wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_name: qwen3-0.6B-pretraining_full_2

outputs/qwen3_pretraining_full_2

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: 5e-06
train_batch_size: 1
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 16
total_eval_batch_size: 16
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: 400
training_steps: 1500

Training results

Framework versions

Transformers 4.51.3
Pytorch 2.5.1+cu121
Datasets 3.5.1
Tokenizers 0.21.1

Downloads last month: 5

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for timarni/qwen3_pretraining_full_2_300

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

(594)

this model

Finetunes

2 models