Instructions to use Tung177/Qwen3-1.7B-KmVi-500K-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tung177/Qwen3-1.7B-KmVi-500K-v0.2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-1.7B-Base")
model = PeftModel.from_pretrained(base_model, "Tung177/Qwen3-1.7B-KmVi-500K-v0.2")

Transformers

How to use Tung177/Qwen3-1.7B-KmVi-500K-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="Tung177/Qwen3-1.7B-KmVi-500K-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tung177/Qwen3-1.7B-KmVi-500K-v0.2")
model = AutoModelForCausalLM.from_pretrained("Tung177/Qwen3-1.7B-KmVi-500K-v0.2", 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 Tung177/Qwen3-1.7B-KmVi-500K-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tung177/Qwen3-1.7B-KmVi-500K-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tung177/Qwen3-1.7B-KmVi-500K-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tung177/Qwen3-1.7B-KmVi-500K-v0.2

SGLang

How to use Tung177/Qwen3-1.7B-KmVi-500K-v0.2 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 "Tung177/Qwen3-1.7B-KmVi-500K-v0.2" \
    --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": "Tung177/Qwen3-1.7B-KmVi-500K-v0.2",
		"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 "Tung177/Qwen3-1.7B-KmVi-500K-v0.2" \
        --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": "Tung177/Qwen3-1.7B-KmVi-500K-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tung177/Qwen3-1.7B-KmVi-500K-v0.2 with Docker Model Runner:
```
docker model run hf.co/Tung177/Qwen3-1.7B-KmVi-500K-v0.2
```

See axolotl config

axolotl version: 0.13.0.dev0

# ====== Model Configuration ======
base_model: Qwen/Qwen3-1.7B-Base
load_in_8bit: false
load_in_4bit: true
strict: false

# LoRA
adapter: qlora
lora_r: 32
lora_alpha: 32
lora_target_linear: true
lora_qkv_kernel: true
lora_o_kernel: true
lora_mlp_kernel: true
embeddings_skip_upcast: true

# Integration
xformers_attention: true
plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
flash_attention: false

# DeepSpeed
# deepspeed: "/kaggle/working/axolotl/deepspeed_configs/zero2_torch_compile.json"

# ====== Hyperparameter Configuration ======
sample_packing: true
learning_rate: 1.8e-4
sequence_len: 4096
micro_batch_size: 8
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

optimizer: adamw_8bit
lr_scheduler: cosine_with_min_lr
lr_scheduler_kwargs: {"min_lr_rate": 0.1}

warmup_ratio: 0.03
weight_decay: 0.01
fp16: true

max_grad_norm: 1.0
num_epochs: 1
save_total_limit: 2
saves_per_epoch: 1
logging_steps: 1

output_dir: /kaggle/working/outputs/qwen-sft-mt-kmvi
chat_template: qwen3

# Dataset
datasets:
  - path: Tung177/km-vi-translation
    type: chat_template
    split: "train"
    roles_to_train: ["assistant"]
dataset_prepared_path: last_run_prepared

dataloader_prefetch_factor: 8
dataloader_num_workers: 2
dataloader_pin_memory: true

# ====== Tracking ======
wandb_project: MT-V1
wandb_name: qwen3-1.7B-mt-kmvi-v0.2
wandb_log_model: "false"

kaggle/working/outputs/qwen-sft-mt-kmvi

This model is a fine-tuned version of Qwen/Qwen3-1.7B-Base on the Tung177/km-vi-translation 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: 0.00018
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 16
total_eval_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine_with_min_lr
lr_scheduler_warmup_steps: 36
training_steps: 1230
mixed_precision_training: Native AMP

Training results

Framework versions

PEFT 0.17.1
Transformers 4.57.1
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.22.1

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Qwen3-1.7B-KmVi-500K-v0.2

kaggle/working/outputs/qwen-sft-mt-kmvi

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for Tung177/Qwen3-1.7B-KmVi-500K-v0.2

Dataset used to train Tung177/Qwen3-1.7B-KmVi-500K-v0.2