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Model: afrideva/phi-2-meditron-GGUF Source: Original Platform
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README.md
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---
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base_model: malhajar/phi-2-meditron
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datasets:
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- epfl-llm/guidelines
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inference: false
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language:
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- en
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license: ms-pl
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model_creator: malhajar
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model_name: phi-2-meditron
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pipeline_tag: text-generation
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quantized_by: afrideva
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tags:
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- Medicine
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- gguf
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- ggml
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- quantized
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- q2_k
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- q3_k_m
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- q4_k_m
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- q5_k_m
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- q6_k
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- q8_0
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---
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# malhajar/phi-2-meditron-GGUF
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Quantized GGUF model files for [phi-2-meditron](https://huggingface.co/malhajar/phi-2-meditron) from [malhajar](https://huggingface.co/malhajar)
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [phi-2-meditron.fp16.gguf](https://huggingface.co/afrideva/phi-2-meditron-GGUF/resolve/main/phi-2-meditron.fp16.gguf) | fp16 | 5.56 GB |
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| [phi-2-meditron.q2_k.gguf](https://huggingface.co/afrideva/phi-2-meditron-GGUF/resolve/main/phi-2-meditron.q2_k.gguf) | q2_k | 1.17 GB |
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| [phi-2-meditron.q3_k_m.gguf](https://huggingface.co/afrideva/phi-2-meditron-GGUF/resolve/main/phi-2-meditron.q3_k_m.gguf) | q3_k_m | 1.48 GB |
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| [phi-2-meditron.q4_k_m.gguf](https://huggingface.co/afrideva/phi-2-meditron-GGUF/resolve/main/phi-2-meditron.q4_k_m.gguf) | q4_k_m | 1.79 GB |
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| [phi-2-meditron.q5_k_m.gguf](https://huggingface.co/afrideva/phi-2-meditron-GGUF/resolve/main/phi-2-meditron.q5_k_m.gguf) | q5_k_m | 2.07 GB |
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| [phi-2-meditron.q6_k.gguf](https://huggingface.co/afrideva/phi-2-meditron-GGUF/resolve/main/phi-2-meditron.q6_k.gguf) | q6_k | 2.29 GB |
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| [phi-2-meditron.q8_0.gguf](https://huggingface.co/afrideva/phi-2-meditron-GGUF/resolve/main/phi-2-meditron.q8_0.gguf) | q8_0 | 2.96 GB |
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## Original Model Card:
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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phi-2-meditron is a finetuned version of [`epfl-llm/meditron-7b`](https://huggingface.co/epfl-llm/meditron-7b) using SFT Training on the Meditron Dataset.
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This model can answer information about different excplicit ideas in medicine (see [`epfl-llm/meditron-7b`](https://huggingface.co/epfl-llm/meditron-7b) for more info)
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### Model Description
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- **Finetuned by:** [`Mohamad Alhajar`](https://www.linkedin.com/in/muhammet-alhajar/)
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- **Language(s) (NLP):** English
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- **Finetuned from model:** [`microsoft/phi-2`](https://huggingface.co/microsoft/phi-2)
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### Prompt Template
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```
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### Instruction:
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<prompt> (without the <>)
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### Response:
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```
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## How to Get Started with the Model
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Use the code sample provided in the original post to interact with the model.
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```python
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from transformers import AutoTokenizer,AutoModelForCausalLM
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model_id = "malhajar/phi-2-meditron"
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model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
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device_map="auto",
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torch_dtype=torch.float16,
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trust_remote_code= True,
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revision="main")
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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question: "what is tract infection?"
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# For generating a response
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prompt = '''
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### Instruction:
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{question}
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### Response:'''
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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output = model.generate(inputs=input_ids,max_new_tokens=512,pad_token_id=tokenizer.eos_token_id,top_k=50, do_sample=True,
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top_p=0.95)
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response = tokenizer.decode(output[0])
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print(response)
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```
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phi-2-meditron.fp16.gguf
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phi-2-meditron.q2_k.gguf
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phi-2-meditron.q4_k_m.gguf
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phi-2-meditron.q5_k_m.gguf
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phi-2-meditron.q6_k.gguf
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phi-2-meditron.q8_0.gguf
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phi-2-meditron.q8_0.gguf
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