---
title: Inixion-2x8B-AWQ
canonical_url: "https://www.modelscope.cn/models/solidrust/Inixion-2x8B-AWQ"
md_url: "https://www.modelscope.cn/models/solidrust/Inixion-2x8B-AWQ.md"
repository: solidrust/Inixion-2x8B-AWQ
last_updated: 2026-08-26
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - mixtral
architectures:
  - MixtralForCausalLM
base_model:
  - Alsebay/Inixion-2x8B
base_model_relation: quantized
parameters: 13.7B
tensor_type:
  - F16
  - I32
library_name:
  - transformer
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 20
stars: 0
tags:
  - 4-bit
  - AWQ
  - text-generation
  - autotrain_compatible
  - endpoints_compatible
---

# Inixion-2x8B-AWQ

> Inixion-2x8B-AWQ - solidrust 在 ModelScope 开源的模型。Alsebay/Inixion-2x8B AWQ

solidrust/Inixion-2x8B-AWQ 是 ModelScope 魔搭社区上的 13.7B 参数text-generation模型，基于 Alsebay/Inixion-2x8B 构建。

- **Repository**: solidrust/Inixion-2x8B-AWQ
- **Tasks**: text-generation
- **Parameters**: 13.7B
- **Base model**: Alsebay/Inixion-2x8B
- **Tags**: 4-bit, AWQ, text-generation, autotrain_compatible, endpoints_compatible
- **Downloads**: 20
- **Stars**: 0
- **Last updated**: 2026-08-26

Source: https://www.modelscope.cn/models/solidrust/Inixion-2x8B-AWQ

---

# Alsebay/Inixion-2x8B AWQ

- Model creator: [Alsebay](https://huggingface.co/Alsebay)
- Original model: [Inixion-2x8B](https://huggingface.co/Alsebay/Inixion-2x8B)



## How to use

### Install the necessary packages

```bash
pip install --upgrade autoawq autoawq-kernels
```

### Example Python code

```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer

model_path = "solidrust/Inixion-2x8B-AWQ"
system_message = "You are Inixion-2x8B, incarnated as a powerful AI. You were created by Alsebay."

# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
                                          fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
                                          trust_remote_code=True)
streamer = TextStreamer(tokenizer,
                        skip_prompt=True,
                        skip_special_tokens=True)

# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""

prompt = "You're standing on the surface of the Earth. "\
        "You walk one mile south, one mile west and one mile north. "\
        "You end up exactly where you started. Where are you?"

tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
                  return_tensors='pt').input_ids.cuda()

# Generate output
generation_output = model.generate(tokens,
                                  streamer=streamer,
                                  max_new_tokens=512)
```

### About AWQ

AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.

It is supported by:

- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code