tiny-random/gpt-oss-bf16

This tiny model is for debugging. It is randomly initialized with the config adapted from openai/gpt-oss-120b.

Note: This model is in BF16; quantized MXFP4 FFN is not used.

Example usage:

• vLLM

vllm serve tiny-random/gpt-oss-bf16

• Transformers

import torch
from transformers import pipeline

model_id = "tiny-random/gpt-oss-bf16"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype=torch.bfloat16,
    device_map="cuda"
)

messages = [
    {"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
]

outputs = pipe(
    messages,
    max_new_tokens=16,
)
print(outputs[0]["generated_text"][-1])

Codes to create this repo:

import json

import torch
from huggingface_hub import hf_hub_download
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoProcessor,
    AutoTokenizer,
    GenerationConfig,
    GptOssForCausalLM,
    pipeline,
    set_seed,
)

source_model_id = "openai/gpt-oss-120b"
save_folder = "/tmp/tiny-random/gpt-oss-bf16"

processor = AutoProcessor.from_pretrained(source_model_id)
processor.save_pretrained(save_folder)

with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r') as f:
    config_json = json.load(f)
config_json.update({
    "head_dim": 32,
    "hidden_size": 32,  # required by Mxfp4GptOssExperts codes
    "intermediate_size": 64,
    "layer_types": ["sliding_attention", "full_attention"],
    "num_attention_heads": 2,
    "num_hidden_layers": 2,
    "num_key_value_heads": 1,
    "num_local_experts": 32,
    "tie_word_embeddings": True,
})
quantization_config = config_json['quantization_config']
del config_json['quantization_config']
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
    json.dump(config_json, f, indent=2)

config = AutoConfig.from_pretrained(save_folder)
print(config)
torch.set_default_dtype(torch.bfloat16)
model = AutoModelForCausalLM.from_config(config)
torch.set_default_dtype(torch.float32)
model.generation_config = GenerationConfig.from_pretrained(
    source_model_id, trust_remote_code=True,
)
set_seed(42)
with torch.no_grad():
    for name, p in sorted(model.named_parameters()):
        torch.nn.init.normal_(p, 0, 0.1)
        print(name, p.shape)
model.save_pretrained(save_folder)

# mxfp4
from transformers.quantizers.quantizer_mxfp4 import Mxfp4HfQuantizer
# model = AutoModelForCausalLM.from_pretrained(save_folder, trust_remote_code=True, torch_dtype=torch.bfloat16, quantization_config=quantization_config)
# model.save_pretrained(save_folder, safe_serialization=True)