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--- |
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license: wtfpl |
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datasets: |
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- HuggingFaceH4/CodeAlpaca_20K |
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pipeline_tag: text-generation |
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thumbnail: https://huggingface.co/mrm8488/mamba-coder/resolve/main/mamba-coder-no-bg.png |
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language: |
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- en |
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- code |
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--- |
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# Mamba-Coder |
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## MAMBA (2.8B) 🐍 fine-tuned on CodeAlpaca_20k for code generation |
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<div style="text-align:center;width:250px;height:250px;"> |
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<img src="https://huggingface.co/mrm8488/mamba-coder/resolve/main/mamba-coder-no-bg.png" alt="mamba-coder logo""> |
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</div> |
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## Base model info |
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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. |
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It is based on the line of progress on [structured state space models](https://github.com/state-spaces/s4), |
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with an efficient hardware-aware design and implementation in the spirit of [FlashAttention](https://github.com/Dao-AILab/flash-attention). |
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## Dataset info |
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[CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K): contains 20K instruction-following data used for fine-tuning the Code Alpaca model. |
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## Usage |
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```sh |
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pip install torch==2.1.0 transformers==4.35.0 causal-conv1d==1.0.0 mamba-ssm==1.0.1 |
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``` |
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```py |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel |
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CHAT_TEMPLATE_ID = "HuggingFaceH4/zephyr-7b-beta" |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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model_name = "mrm8488/mamba-coder" |
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eos_token = "<|endoftext|>" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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tokenizer.eos_token = eos_token |
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tokenizer.pad_token = tokenizer.eos_token |
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tokenizer.chat_template = AutoTokenizer.from_pretrained(CHAT_TEMPLATE_ID).chat_template |
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model = MambaLMHeadModel.from_pretrained( |
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model_name, device=device, dtype=torch.float16) |
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messages = [] |
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prompt = "Write a bash script to remove .tmp files" |
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messages.append(dict(role="user", content=prompt)) |
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input_ids = tokenizer.apply_chat_template( |
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messages, return_tensors="pt", add_generation_prompt=True |
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).to(device) |
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out = model.generate( |
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input_ids=input_ids, |
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max_length=2000, |
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temperature=0.9, |
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top_p=0.7, |
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eos_token_id=tokenizer.eos_token_id, |
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) |
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decoded = tokenizer.batch_decode(out) |
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assistant_message = ( |
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decoded[0].split("<|assistant|>\n")[-1].replace(eos_token, "") |
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) |
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print(assistant_message) |
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``` |
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## Gradio Demo |
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```sh |
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git clone https://github.com/mrm8488/mamba-chat.git |
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cd mamba-chat |
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pip install -r requirements.txt |
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pip install -q gradio==4.8.0 |
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python app.py \ |
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--model mrm8488/mamba-coder \ |
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--share |
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``` |
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## Evaluations |
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Coming soon! |
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## Citation |
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```Bibtext |
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@misc {manuel_romero_2024, |
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author = { {Manuel Romero} }, |
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title = { mamba-coder (Revision 214a13a) }, |
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year = 2024, |
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url = { https://huggingface.co/mrm8488/mamba-coder }, |
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doi = { 10.57967/hf/1673 }, |
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publisher = { Hugging Face } |
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} |
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``` |
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## Acknowledgments |
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Thanks to [mamba-chat](https://github.com/havenhq/mamba-chat/tree/main) for heavily inspiring our work |
