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---
license: wtfpl
datasets:
- HuggingFaceH4/no_robots
pipeline_tag: text-generation
---
# MAMBA (2.8B) 🐍 fine-tuned on H4/no_robots dataset for chat / instruction
TBD
## Usage
```py
from transformers import AutoTokenizer, AutoModelForCausalLM
from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel
CHAT_TEMPLATE_ID = "HuggingFaceH4/zephyr-7b-beta"
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="cuda", dtype=torch.float16)
history_dict: list[dict[str, str]] = []
prompt = "Tell me 5 sites to visit in Spain"
history_dict.append(dict(role="user", content=prompt))
input_ids = tokenizer.apply_chat_template(
history_dict, 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, "")
)
print(assistant_message)
```
## Evaluations
Coming soon!
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