π Mamba fine-tuned models
Collection
A collection with ClibrAIn's Mamba fine-tuned models
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3 items
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Updated
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11
Model Card is still WIP!
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.
Not Provided
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-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)
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
Coming soon!
Thanks to mamba-chat for heavily inspiring our work