fr it de es en zh license:apache-2.0 region:us
---
license: apache-2.0
language:
- fr
- it
- de
- es
- en
- zh
inference: false
---
Copy from this [model card](https://huggingface.co/TimeMobius/Mobius-12B-base-m1)

# Model Card for Mobius-12B-base-m1 
The Mobius-12B-base-m1 Large Language Model (LLM) is a pretrained model based on RWKV v5 arch.
We utilized 0.01 billion tokens to conduct post-training on this model for alignment benchmarks, excluding the utilization of [DPO and SFT](https://github.com/BBuf/trl/pull/1). The process took approximately 10 hours, employing 4 * a800.


## Warning
This repo contains weights that are not compatible with Hugging Face [transformers](https://github.com/huggingface/transformers) library yet. But you can try this[PR](https://github.com/huggingface/transformers/pull/26963) as well. 
 [RWKV runner]() or [AI00 server]() also work.

## Instruction|Chat format

This format must be strictly respected, otherwise the model will generate sub-optimal outputs.

The template used to build a prompt for the Instruct model is defined as follows:
```
User: {Instruction|prompt}\n\nAssistant:
```

## Run the model

[need to convert checkpoint to HF format](https://github.com/xiaol/RWKV-World-HF-Tokenizer?tab=readme-ov-file#huggingface-rwkv-world-model-convert)

Need to install this [PR](https://github.com/huggingface/transformers/pull/26963)
pip install -e git://github.com/BBuf/transformers.git 

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("TimeMobius/Mobius-12B-base-m1", torch_dtype=torch.float16).to(0)
tokenizer = AutoTokenizer.from_pretrained("TimeMobius/Mobius-12B-base-m1", trust_remote_code=True)

text = "x"
prompt = f'Question: {text.strip()}\n\nAnswer:'

inputs = tokenizer(prompt, return_tensors="pt").to(0)
output = model.generate(inputs["input_ids"], max_new_tokens=40)
print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
```

## Limitations

The Mobius base m1 is the base model can be easily fine-tuned to achieve compelling performance. 
if you wanna better benchmark results use [DPO and SFT](https://github.com/BBuf/trl/pull/1) ,details in [readme](https://github.com/BBuf/trl/pull/1/files)

### Benchmark

| Mobius-12B-base-m1 |          | 
|--------------------|----------|
| lambda ppl                | 3.41   |
| lambda                |  0.72  |
| piqa               | 0.78   |
| hellaswag 10 shots         | 0.72        |
| winogrande         | 0.68        |
| arc_challenge 25shots     | 0.47       | 
| arc_easy           | 0.73       |
| openbookqa         | 0.40       |
| sciq               | 0.93       |


# @TimeMobius

