---
license: wtfpl
datasets:
- HuggingFaceH4/no_robots
pipeline_tag: text-generation
---

# MAMBA (2.8B) 🐍 fine-tuned on H4/no_robots dataset for chat / instruction

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

_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

```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 = torch.device("cuda" if torch.cuda.is_available() else "cpu")

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)

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

input_ids = tokenizer.apply_chat_template(
            history_dict, 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, "")
)

print(assistant_message)
```

## Evaluations

Coming soon!