Instructions to use timarni/qwen3_reasoning_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use timarni/qwen3_reasoning_sft with Transformers:

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

pipe = pipeline("text-generation", model="timarni/qwen3_reasoning_sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("timarni/qwen3_reasoning_sft")
model = AutoModelForCausalLM.from_pretrained("timarni/qwen3_reasoning_sft")
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 Settings

vLLM

How to use timarni/qwen3_reasoning_sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "timarni/qwen3_reasoning_sft"
# 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_reasoning_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/timarni/qwen3_reasoning_sft

SGLang

How to use timarni/qwen3_reasoning_sft 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_reasoning_sft" \
    --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_reasoning_sft",
		"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_reasoning_sft" \
        --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_reasoning_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use timarni/qwen3_reasoning_sft with Docker Model Runner:
```
docker model run hf.co/timarni/qwen3_reasoning_sft
```

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/reasoning_SFT
    type: chat_template
    split: train
    field_messages: conversations
    # message_property_mappings:
    #   role: from
    #   content: value

val_set_size: 0.1

output_dir: ./outputs/qwen3_reasoning_sft
dataset_prepared_path: last_run_prepared

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

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

wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_watch:
wandb_name: qwen3_reasoning_sft
wandb_log_model:

gradient_accumulation_steps: 2 # 16 following https://unsloth.ai/blog/qwen3
micro_batch_size: 1 # 2
num_epochs: 6
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00005 # 0.0002
cosine_min_lr_ratio: 0.1

bf16: auto
tf32: true

gradient_checkpointing: offload
logging_steps: 1
gradient_clipping: 1.0
flash_attention: true

warmup_ratio: 0.03
evals_per_epoch: 4
saves_per_epoch: 2
save_total_limit: 25
weight_decay: 1e-4
special_tokens:

outputs/qwen3_reasoning_sft

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

Loss: 0.8020

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: 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: 47
num_epochs: 6.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.965	0.0037	1	0.8999
0.8101	0.2505	67	0.7453
0.6077	0.5009	134	0.7342
0.5874	0.7514	201	0.7270
0.4362	1.0	268	0.7260
0.6779	1.2505	335	0.7269
0.505	1.5009	402	0.7310
0.4969	1.7514	469	0.7274
0.309	2.0	536	0.7332
0.5954	2.2505	603	0.7428
0.4302	2.5009	670	0.7514
0.4301	2.7514	737	0.7491
0.23	3.0	804	0.7559
0.5296	3.2505	871	0.7683
0.3761	3.5009	938	0.7857
0.3916	3.7514	1005	0.7818
0.1842	4.0	1072	0.7863
0.4926	4.2505	1139	0.7980
0.3469	4.5009	1206	0.8004
0.3697	4.7514	1273	0.7908
0.1665	5.0	1340	0.7925
0.4773	5.2505	1407	0.8187
0.3364	5.5009	1474	0.8071
0.3622	5.7514	1541	0.8020

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/qwen3_reasoning_sft

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

(630)

this model

timarni
/

qwen3_reasoning_sft

outputs/qwen3_reasoning_sft

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for timarni/qwen3_reasoning_sft

Dataset used to train timarni/qwen3_reasoning_sft