Qwen3-8B-Instruct

This model is the language model component extracted from Qwen/Qwen3-VL-8B-Instruct, a vision-language model.

The vision components have been removed, leaving only the pure text-generation LLM, which can be used independently for text-only tasks.

Model Details

• Base Model: Qwen3-VL-8B-Instruct (language component only)
• Model Type: Qwen3ForCausalLM
• Parameters: ~8.2B (8,190,735,360)
• Model Size: ~16GB
• Precision: bfloat16
• License: Apache 2.0

Architecture

• Hidden Size: 4096
• Intermediate Size: 12288
• Number of Layers: 36
• Attention Heads: 32 (8 KV heads, GQA)
• Head Dimension: 128
• Vocabulary Size: 151,936
• Max Position Embeddings: 262,144
• RoPE Theta: 5,000,000

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "alexchen4ai/Qwen3-8B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of France?"}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Extraction Process

This model was extracted from Qwen3-VL-8B-Instruct by:

1. Loading all safetensors shards from the original model
2. Filtering and extracting only the model.language_model.* weights
3. Renaming keys to standard Qwen3 format (model.*)
4. Preserving the lm_head for token prediction
5. Creating a compatible Qwen3ForCausalLM config
6. Copying tokenizer files and generation config

Differences from Original

• Removed: All vision encoder components (model.visual.*)
• Removed: Vision-language projection layers
• Kept: Pure language model transformer layers
• Kept: Token embeddings and LM head
• Kept: All tokenizer files

Use Cases

This extracted model is suitable for:

• Pure text generation tasks
• Instruction following
• Chat applications
• Fine-tuning on text-only datasets
• Integration with frameworks expecting standard causal LMs
• Lower memory usage compared to the full VL model

Limitations

• This model does not support vision inputs (images/videos)
• For vision-language tasks, use the original Qwen3-VL-8B-Instruct

Citation

If you use this model, please cite the original Qwen3-VL work:

@article{qwen3vl,
  title={Qwen3-VL: Towards Versatile Vision-Language Understanding},
  author={Qwen Team},
  year={2024}
}

Acknowledgments

• Original model by Qwen Team / Alibaba Cloud
• Extraction performed for easier deployment in text-only scenarios