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---
license: wtfpl
datasets:
- HuggingFaceH4/no_robots
thumbnail: https://huggingface.co/clibrain/mamba-2.8b-chat-no_robots/resolve/main/mamba_no_robos-logo.png
pipeline_tag: text-generation
language:
- en
---
# MAMBA (2.8B) ๐Ÿ fine-tuned on H4/no_robots dataset for chat / instruction
Model Card is still WIP!
<div style="text-align:center;width:250px;height:250px;">
<img src="https://huggingface.co/clibrain/mamba-2.8b-chat-no_robots/resolve/main/mamba_no_robos-logo.png" alt="mamba-no_robots logo"">
</div>
## 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
_Look Ma, an instruction dataset that wasn't generated by GPTs!_
### Dataset Description
- **Repository:** https://github.com/huggingface/alignment-handbook
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** Lewis Tunstall
#### Dataset Summary
No Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's [InstructGPT paper](https://huggingface.co/papers/2203.02155), and is comprised mostly of single-turn instructions across the following categories:
| Category | Count |
|:-----------|--------:|
| Generation | 4560 |
| Open QA | 1240 |
| Brainstorm | 1120 |
| Chat | 850 |
| Rewrite | 660 |
| Summarize | 420 |
| Coding | 350 |
| Classify | 350 |
| Closed QA | 260 |
| Extract | 190 |
## 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-chat-no_robots"
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-chat-no_robots \
--share
```
## Evaluations
Coming soon!
## Acknowledgments
Thanks to [mamba-chat](https://github.com/havenhq/mamba-chat/tree/main) for heavily inspiring our work