pytorch en dataset:migtissera/Synthia-v1.3 license:wtfpl region:us
---
license: wtfpl
thumbnail: https://huggingface.co/clibrain/mamba-2.8b-ft-synthia-v1.3/resolve/main/mamba-syn-removebg-preview.png?download=true
datasets:
- migtissera/Synthia-v1.3
language:
- en
---


# MAMBA (2.8B) 🐍 fine-tuned on Synthia-v1.3

<div style="text-align:center;width:250px;height:250px;">
    <img src="https://huggingface.co/clibrain/mamba-2.8b-ft-synthia-v1.3/resolve/main/mamba-syn-removebg-preview.png" alt="mamba-hermes logo"">
</div>

Model Card is still WIP!


## Base model info

Mamba is a new state space model architecture showing promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of Transformers.
It is based on the line of progress on [structured state space models](https://github.com/state-spaces/s4),
with an efficient hardware-aware design and implementation in the spirit of [FlashAttention](https://github.com/Dao-AILab/flash-attention).

## Dataset info

Not Provided

## Usage

```sh
pip install torch==2.1.0 transformers==4.35.0 causal-conv1d==1.0.0 mamba-ssm==1.0.1
```

```py
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel

CHAT_TEMPLATE_ID = "HuggingFaceH4/zephyr-7b-beta"

device = "cuda:0" if torch.cuda.is_available() else "cpu"
model_name = "clibrain/mamba-2.8b-ft-synthia-v1.3"

eos_token = "<|endoftext|>"
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.eos_token = eos_token
tokenizer.pad_token = tokenizer.eos_token
tokenizer.chat_template = AutoTokenizer.from_pretrained(CHAT_TEMPLATE_ID).chat_template

model = MambaLMHeadModel.from_pretrained(
        model_name, device=device, dtype=torch.float16)

messages = []
prompt = "Tell me 5 sites to visit in Spain"
messages.append(dict(role="user", content=prompt))

input_ids = tokenizer.apply_chat_template(
            messages, return_tensors="pt", add_generation_prompt=True
).to(device)

out = model.generate(
    input_ids=input_ids,
    max_length=2000,
    temperature=0.9,
    top_p=0.7,
    eos_token_id=tokenizer.eos_token_id,
)

decoded = tokenizer.batch_decode(out)
assistant_message = (
    decoded[0].split("<|assistant|>\n")[-1].replace(eos_token, "")
)

print(assistant_message)
```


## Gradio Demo

```sh
git clone https://github.com/mrm8488/mamba-chat.git
cd mamba-chat

pip install -r requirements.txt
pip install -q gradio==4.8.0

python app.py \
--model clibrain/mamba-2.8b-ft-synthia-v1.3 \
--share
```
## Evaluations

Coming soon!


## Acknowledgments

Thanks to [mamba-chat](https://github.com/havenhq/mamba-chat/tree/main) for heavily inspiring our work