Mamba 2.8b Instruct Openhermes

🚀 MAMBA (2.8B) 🐍 fine-tuned on OpenHermes

This project is a fine-tuned version of MAMBA (2.8B) on the OpenHermes dataset, aiming to enhance text generation capabilities.

🚀 Quick Start

Installation

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

Basic Usage

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-instruct-openhermes"

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-instruct-openhermes \
--share

✨ Features

Base Model Features

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 Features

The OpenHermes dataset is composed of 242,000 entries of primarily GPT-4 generated data, from open datasets across the AI landscape, including:

• GPTeacher - General Instruct, Roleplay v1, Roleplay v2, and Code Instruct Datasets, by Teknium
• WizardLM (v1, evol_instruct 70k), by WizardLM Team/nlpxucan
• Airoboros GPT-4 (v1.0), by JonDurbin
• Camel-AI's domain expert datasets, by the Camel-AI Team
• CodeAlpaca, by Sahil2801
• GPT4-LLM and Unnatural Instructions, by Microsoft

Filtering included removal of OpenAI refusals, disclaimers, and "As an AI" type examples and more. The base dataset mix is identical to the original Nous-Hermes', minus the Nous-Instruct and PDACTL datasets which were private datasets.

📚 Documentation

Model Card

Model Card is still WIP!

Evaluations

Evaluations are coming soon!

📄 License

This project is licensed under the WTFPL license.

Acknowledgments

Thanks to mamba-chat for heavily inspiring our work.