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README.md
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# MAMBA (2.8B) 🐍 fine-tuned on H4/no_robots dataset for chat / instruction
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## Usage
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```py
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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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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.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=
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history_dict: list[dict[str, str]] = []
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prompt = "Tell me 5 sites to visit in Spain"
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input_ids = tokenizer.apply_chat_template(
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history_dict, return_tensors="pt", add_generation_prompt=True
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out = model.generate(
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input_ids=input_ids,
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# MAMBA (2.8B) 🐍 fine-tuned on H4/no_robots dataset for chat / instruction
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Model Card is still WIP!
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## Usage
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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 = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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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.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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history_dict: list[dict[str, str]] = []
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prompt = "Tell me 5 sites to visit in Spain"
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input_ids = tokenizer.apply_chat_template(
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history_dict, 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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