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, with an efficient hardware-aware design and implementation in the spirit of FlashAttention.
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, 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
pip install torch==2.1.0 transformers==4.35.0 causal-conv1d==1.0.0 mamba-ssm==1.0.1
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
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 for heavily inspiring our work