Create README.md

README.md

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+---
+lang: eng
+---
+
+# Model Card for Puffin-Phi V2
+
+This is my first fine-tune of Puffin that seems to be working fairly reliably! 
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/8amP4DRFWEBFOkfb2CQpD.png)
+
+## Model Details
+
+### Model Sources [optional]
+
+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:
+```
+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
+