---
license: apache-2.0
base_model: Qwen/Qwen3-VL-8B-Instruct
tags:
- qwen3
- text-generation
- llm
- extracted
language:
- en
- zh
pipeline_tag: text-generation
---

# Qwen3-8B-Instruct

This model is the **language model component** extracted from [Qwen/Qwen3-VL-8B-Instruct](https://huggingface.co/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

```python
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](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct)

## Citation

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

```bibtex
@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
