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