Instructions to use winglian/qwen3-14b-triton-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use winglian/qwen3-14b-triton-v1 with Transformers:

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

pipe = pipeline("text-generation", model="winglian/qwen3-14b-triton-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("winglian/qwen3-14b-triton-v1")
model = AutoModelForCausalLM.from_pretrained("winglian/qwen3-14b-triton-v1", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use winglian/qwen3-14b-triton-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "winglian/qwen3-14b-triton-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "winglian/qwen3-14b-triton-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/winglian/qwen3-14b-triton-v1

SGLang

How to use winglian/qwen3-14b-triton-v1 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 "winglian/qwen3-14b-triton-v1" \
    --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": "winglian/qwen3-14b-triton-v1",
		"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 "winglian/qwen3-14b-triton-v1" \
        --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": "winglian/qwen3-14b-triton-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use winglian/qwen3-14b-triton-v1 with Docker Model Runner:
```
docker model run hf.co/winglian/qwen3-14b-triton-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

See axolotl config

axolotl version: 0.10.0.dev0

base_model: Qwen/Qwen3-14B-Base

plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true

chat_template: qwen3 
datasets:
  - path: axolotl-ai-internal/gpumode-py2triton-reasoning-v2
    type: chat_template
    split: train
    split_thinking: true
    eot_tokens: ["<|im_end|>"]

dataset_prepared_path: last_run_prepared
val_set_size: 0.005
output_dir: ./outputs/out
save_only_model: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

wandb_project: qwen3-14b-grpo-triton
wandb_entity: axolotl-ai
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_torch_fused
max_grad_norm: 0.1
neftune_noise_alpha: 10
lr_scheduler: cosine
learning_rate: 3e-6

bf16: true
tf32: true

gradient_checkpointing: offload
gradient_checkpointing_kwargs:
  use_reentrant: false
logging_steps: 1
flash_attention: true

warmup_steps: 100
evals_per_epoch: 5
saves_per_epoch: 1
weight_decay: 0.01
deepspeed: deepspeed_configs/zero1.json

outputs/out

This model is a fine-tuned version of Qwen/Qwen3-14B-Base on the axolotl-ai-internal/gpumode-py2triton-reasoning-v2 dataset. It achieves the following results on the evaluation set:

Loss: 0.2053

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: 3e-06
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 16
total_eval_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.4288	0.0039	1	0.5326
0.289	0.2	51	0.3414
0.2091	0.4	102	0.2622
0.2009	0.6	153	0.2362
0.1848	0.8	204	0.2248
0.1654	1.0	255	0.2186
0.1803	1.2	306	0.2165
0.1642	1.4	357	0.2116
0.1714	1.6	408	0.2094
0.164	1.8	459	0.2074
0.1488	2.0	510	0.2069
0.1676	2.2	561	0.2069
0.153	2.4	612	0.2059
0.1621	2.6	663	0.2056
0.1568	2.8	714	0.2055
0.1433	3.0	765	0.2053

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.1
Tokenizers 0.21.1

Downloads last month: 12

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for winglian/qwen3-14b-triton-v1

Base model

Qwen/Qwen3-14B-Base

Finetuned

(82)

this model

Quantizations

1 model

winglian
/

qwen3-14b-triton-v1

outputs/out

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for winglian/qwen3-14b-triton-v1

Dataset used to train winglian/qwen3-14b-triton-v1