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---
license: other
language:
- en
pipeline_tag: text-generation
datasets:
- LDJnr/Puffin
---
# Model Card for Puffin-Phi V2
This is my first fine-tune of Puffin that seems to be working fairly reliably!
## Model Details
### Model Sources
This model was trained on the Puffin Dataset, made by LDJ, using a slightly modified version of the dataset that removed >2000 token entries, so there would be no early cutoffs during training phi, since it's context is 2k.
## Uses
Let me know!
## How to Get Started with the Model
Phi does not support device_map "auto", and does not seem to want to inference in fp16, so use bf16.
Here is working code to inference, though it can be improved:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
sysprompt = "The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
model = AutoModelForCausalLM.from_pretrained("teknium/Puffin-Phi-v2", trust_remote_code=True, torch_dtype=torch.bfloat16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("teknium/Puffin-Phi-v2", trust_remote_code=True, torch_dtype=torch.bfloat16)
inputs = tokenizer(f"{sysprompt}USER: Write a negative review for the website Twitter.\nASSISTANT:", return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=128, do_sample=True, temperature=0.2, top_p=0.9, use_cache=True, repetition_penalty=1.2, eos_token_id=tokenizer.eos_token_id)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
The prompt format is ShareGPT/Vicuna, so it uses the sysprompt (defualt in sysprompt variable)
then is prompted like so:
```
USER: <prompt>
ASSISTANT:
```
## Training Details
### Training Procedure
Trained with Axolotl. View the wandb runs for all my puffin runs (this is puffin-phi-4 on wandb):
https://wandb.ai/teknium1/puffin-phi/runs/puffin-phi-4
## Evaluation
TODO
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