---
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!