Qwen3 Causal Language Model (BF16)
This repository contains a decoder-only causal language model using the Qwen3 architecture. The weights are stored in sharded Safetensors format using BF16 precision.
Model details
| Item | Value |
|---|---|
| Architecture | Qwen3ForCausalLM |
| Model type | qwen3 |
| Parameters | 8,190,735,360 |
| Weight precision | BF16 |
| Hidden size | 4096 |
| Layers | 36 |
| Attention heads | 32 |
| Key/value heads | 8 |
| Vocabulary size | 151,936 |
| Configured context length | 40,960 tokens |
| Weight format | 7 sharded Safetensors files (about 5 GB each) |
The tokenizer configuration currently declares a maximum length of 131,072, while the model configuration declares 40,960 positions. Until the longer context length is independently validated, use 40,960 tokens as the supported maximum.
Usage
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "YOUR_USERNAME/YOUR_MODEL_NAME"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
attn_implementation="sdpa",
)
messages = [{"role": "user", "content": "你好，请介绍一下你自己。"}]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
with torch.inference_mode():
output = model.generate(**inputs, max_new_tokens=256)
answer = output[0, inputs.input_ids.shape[1]:]
print(tokenizer.decode(answer, skip_special_tokens=True))
BF16 weights require roughly 16 GB just for the model parameters, plus
additional memory for inference state and the KV cache. Use a machine with
sufficient RAM or accelerator memory. The example selects SDPA so that
FlashAttention 2 is not required; installations with FlashAttention 2 may
choose attn_implementation="flash_attention_2" instead.
Training and provenance
The files supplied with this model do not identify the following information:
- Base model or checkpoint
- Training or fine-tuning method
- Training datasets
- Evaluation results
- Model author or organization
The uploader should add these details before publishing so users can assess the model's capabilities, limitations, and provenance.
License
No license information was included with the supplied model files. The uploader must verify the licenses of the base model, training data, and resulting weights and add the correct license before making the repository public. Absence of a license does not grant permission to use, modify, or redistribute the model.
Limitations
No benchmark, safety, bias, or long-context evaluation results were supplied. Do not rely on this model for high-impact decisions without appropriate testing and human review.