Instructions to use AlexHung29629/gpt-oss-mb-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlexHung29629/gpt-oss-mb-v1 with Transformers:

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

pipe = pipeline("text-generation", model="AlexHung29629/gpt-oss-mb-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AlexHung29629/gpt-oss-mb-v1")
model = AutoModelForCausalLM.from_pretrained("AlexHung29629/gpt-oss-mb-v1")
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 AlexHung29629/gpt-oss-mb-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlexHung29629/gpt-oss-mb-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": "AlexHung29629/gpt-oss-mb-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AlexHung29629/gpt-oss-mb-v1

SGLang

How to use AlexHung29629/gpt-oss-mb-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 "AlexHung29629/gpt-oss-mb-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": "AlexHung29629/gpt-oss-mb-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 "AlexHung29629/gpt-oss-mb-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": "AlexHung29629/gpt-oss-mb-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AlexHung29629/gpt-oss-mb-v1 with Docker Model Runner:
```
docker model run hf.co/AlexHung29629/gpt-oss-mb-v1
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

See axolotl config

axolotl version: 0.13.0.dev0

base_model: ./gpt-oss-20b
use_kernels: false
model_quantization_config: Mxfp4Config
model_quantization_config_kwargs:
  dequantize: true

plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

experimental_skip_move_to_device: true  # prevent OOM by NOT putting model to GPU before sharding

datasets:
  - path: AlexHung29629/mbpii
    type: chat_template

dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/gpt-oss-out/

dataloader_num_workers: 0
dataloader_pin_memory: True

sequence_len: 16384
sample_packing: true
eval_sample_packing: false
remove_unused_columns: false
pad_to_sequence_len: true

wandb_project: mb_pii
wandb_name: v1
tensorboard: true

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1

optimizer: adamw_torch_fused
lr_scheduler: constant_with_warmup
learning_rate: 2e-6

bf16: true
tf32: true

#flash_attention: true
eager_attention: true
#sdp_attention: true
#attn_implementation: kernels-community/vllm-flash-attn3
#flex_attention: true
#flex_attn_compile_kwargs:
#  dynamic: false
#  mode: max-autotune-no-cudagraphs

torch_compile: true

#gradient_checkpointing: true
#activation_offloading: true

logging_steps: 1
saves_per_epoch: 1

warmup_ratio: 0.05

special_tokens:
eot_tokens:
  - "<|end|>"
  - "<|return|>"

fsdp_version: 2
fsdp_config:
  offload_params: false
  state_dict_type: SHARDED_STATE_DICT
  auto_wrap_policy: TRANSFORMER_BASED_WRAP
  transformer_layer_cls_to_wrap: GptOssDecoderLayer
  reshard_after_forward: true
  activation_checkpointing: true

outputs/gpt-oss-out/

This model was trained from scratch on the AlexHung29629/mbpii 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: 2e-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 8
total_eval_batch_size: 8
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: constant_with_warmup
training_steps: 17

Training results

Framework versions

Transformers 4.55.0
Pytorch 2.7.1+cu128
Datasets 4.0.0
Tokenizers 0.21.4

Downloads last month: -

Safetensors

Model size

21B params

Tensor type

BF16