Create README.md

README.md

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+---
+license: mit
+base_model: microsoft/phi-2
+tags:
+- generated_from_trainer
+datasets:
+- teknium/OpenHermes-2.5
+model-index:
+- name: phi-2-OpenHermes-2.5
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# phi-2-OpenHermes-2.5
+This is a GGUF quantize with my favorite quantizations of [minghaowu/phi-2-OpenHermes-2.5](https://huggingface.co/minghaowu/phi-2-OpenHermes-2.5). A phi-2 fine-tuned on [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5). 
+
+I quickly quantized this model using a modified version of [AutoGGUF](https://t.co/oUuxN2fvSX) from [Maxime Labonne](https://huggingface.co/mlabonne)
+
+The prompt format is a little bit guesswork but it seems to work. Here is my Ollama modelfile:
+```
+FROM ./phi-2-openhermes-2.5.Q5_K_M.gguf
+PARAMETER num_ctx 2048
+TEMPLATE """{{ .System }}
+### USER: {{ .Prompt }}<|endoftext|>
+### ASSISTANT:
+"""
+PARAMETER stop "<|endoftext|>"
+```
+
+Many Kudos to [Microsoft](https://huggingface.co/microsoft), [Teknium](https://huggingface.co/datasets/teknium) and [Minghao Wu]((https://huggingface.co/minghaowu)
+
+---
+
+# Original Modelcard
+This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the teknium/OpenHermes-2.5 dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 4
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 128
+- total_eval_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 1.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.16.1
+- Tokenizers 0.15.1
+
+### Inference
+
+``` 
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+model_id = "minghaowu/phi-2-OpenHermes-2.5"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")
+
+your_instruction = <your_instruction>
+infer_prompt = f"### USER: {your_instruction} <|endoftext|>\n### ASSISTANT:"
+output = pipe(infer_prompt, do_sample=True, max_new_tokens=256)[0]["generated_text"]
+print(output)
+```