---
license: apache-2.0
---

![An eagle soaring above a transformer robot](https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F10cf7fd1-6c72-4a99-84c2-794fb7bc52b3_2432x1664.png)

### Huggingface EagleX 1.7T Model - via HF Transformers Library

> **! Important Note !**
>
> The following is the HF transformers implementation of the EagleX 7B 1.7T model. **This is meant to be used with the huggingface transformers**
>
> For the full model weights on its own, to use with other RWKV libraries, refer to [here](https://huggingface.co/recursal/EagleX_1-7T)
>
> This is not an instruct tune model! (soon...)
>
> See the following, for the full details on this experimental model: [https://substack.recursal.ai/p/eaglex-17t-soaring-past-llama-7b](https://substack.recursal.ai/p/eaglex-17t-soaring-past-llama-7b)
>
- [Our cloud platform - the best place to host, finetune, and do inference for RWKV](https://recursal.ai)
- [HF Demo](https://huggingface.co/spaces/recursal/EagleX-7B-1.7T-Gradio-Demo)
- [Our wiki](https://wiki.rwkv.com)
- [pth model weights](https://huggingface.co/recursal/EagleX_1-7)

#### Running on GPU via HF transformers

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

def generate_prompt(instruction, input=""):
    instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
    input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
    if input:
        return f"""Instruction: {instruction}

Input: {input}

Response:"""
    else:
        return f"""User: hi

Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.

User: {instruction}

Assistant:"""


model = AutoModelForCausalLM.from_pretrained("recursal/EagleX_1-7T_HF", trust_remote_code=True, torch_dtype=torch.float16).to(0)
tokenizer = AutoTokenizer.from_pretrained("recursal/EagleX_1-7T_HF", trust_remote_code=True)

text = "Tell me a fun fact"
prompt = generate_prompt(text)

inputs = tokenizer(prompt, return_tensors="pt").to(0)
output = model.generate(inputs["input_ids"], max_new_tokens=128, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, )
print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
```

output:

```shell
User: hi

Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.

User: Tell me a fun fact

Assistant: Did you know that the human brain has 100 billion neurons?
```