Instructions to use timarni/qwen3-0.6B-reasoning-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use timarni/qwen3-0.6B-reasoning-sft with Transformers:

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

pipe = pipeline("text-generation", model="timarni/qwen3-0.6B-reasoning-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("timarni/qwen3-0.6B-reasoning-sft")
model = AutoModelForCausalLM.from_pretrained("timarni/qwen3-0.6B-reasoning-sft", device_map="auto")
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/qwen3-0.6B-reasoning-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "timarni/qwen3-0.6B-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/qwen3-0.6B-reasoning-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/timarni/qwen3-0.6B-reasoning-sft

SGLang

How to use timarni/qwen3-0.6B-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/qwen3-0.6B-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/qwen3-0.6B-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/qwen3-0.6B-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/qwen3-0.6B-reasoning-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use timarni/qwen3-0.6B-reasoning-sft with Docker Model Runner:
```
docker model run hf.co/timarni/qwen3-0.6B-reasoning-sft
```

See axolotl config

axolotl version: 0.10.0.dev0

# Model Configuration
base_model: Qwen/Qwen3-0.6B-Base
type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
special_tokens:
flash_attention: true
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
sequence_len: 4096
chat_template: qwen3

# Dataset Configuration
shuffle_merged_datasets: true
dataset_processes: 8
sample_packing: true
pad_to_sequence_len: true
group_by_length: false
train_on_inputs: false
datasets:
  - path: timarni/s1k_r1_clean
    ds_type: json
    type: chat_template
    field_messages: conversations
    message_property_mappings: {role: from, content: value}

# datasets:
#   - path: "json"
#     data_files: "/mloscratch/users/arni/reasoning_sft/data/s1k_r1/s1k_r1_think_token_cleaned.jsonl"
#     type: chat_template
#     ds_type: json
#     split: train
#     field_messages: conversations
#     message_field_role: from
#     message_field_content: value

# Training Hyperparameters
micro_batch_size: 1
gradient_accumulation_steps: 2
max_steps: 8500
num_epochs: 5
learning_rate: 1e-6 # 7e-7
optimizer: adamw_torch
optim_args:
  fused: true
lr_scheduler: cosine
cosine_min_lr_ratio: 0
warmup_ratio: 0.05
weight_decay: 1.0e-4
adam_beta1: 0.9
adam_beta2: 0.95
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
max_grad_norm: 1.0

# Hardware/Performance Configuration
load_in_4bit: false
load_in_8bit: false
deepspeed: /mloscratch/users/arni/meditron_protocol/training/sft/axolotl_config/deepspeed.json
xformers_attention: null
eager_attention: true
tf32: false
bf16: true

# Logging/Checkpointing
output_dir: /mloscratch/users/arni/models/qwen3-0.6B-Base-s1k_r1_reasoning_token
logging_steps: 1
saves_per_epoch: 1
resume_from_checkpoint: null
load_best_model_at_end: false
early_stopping_patience: 0
eval_set_size: 0.0
eval_table_size: null
# evals_per_epoch: 2
# eval_steps: 1000
# save_steps: 100

# WandB Configuration
wandb_project: mnlp # meditron-reasoning
wandb_entity: tim-arni # alexs-team
wandb_name: qwen3-0.6B-Base-s1k_r1_reasoning_token # medicouenne-7b-checkpoint-5742-medMCQA

mloscratch/users/arni/models/qwen3-0.6B-Base-s1k_r1_reasoning_token

This model is a fine-tuned version of Qwen/Qwen3-0.6B-Base on the /mloscratch/users/arni/reasoning_sft/data/s1k_r1/s1k_r1_think_token_cleaned.jsonl 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-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 2
total_train_batch_size: 2
optimizer: Use adamw_torch with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=fused=True
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 11
training_steps: 8500

Training results

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

Downloads last month: 8

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for timarni/qwen3-0.6B-reasoning-sft

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

(657)

this model