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Model: afrideva/phi-2-meditron-GGUF Source: Original Platform
2026-04-28 17:05:02 +08:00
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+---
+base_model: malhajar/phi-2-meditron
+datasets:
+- epfl-llm/guidelines
+inference: false
+language:
+- en
+license: ms-pl
+model_creator: malhajar
+model_name: phi-2-meditron
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- Medicine
+- gguf
+- ggml
+- quantized
+- q2_k
+- q3_k_m
+- q4_k_m
+- q5_k_m
+- q6_k
+- q8_0
+---
+# malhajar/phi-2-meditron-GGUF
+
+Quantized GGUF model files for [phi-2-meditron](https://huggingface.co/malhajar/phi-2-meditron) from [malhajar](https://huggingface.co/malhajar)
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [phi-2-meditron.fp16.gguf](https://huggingface.co/afrideva/phi-2-meditron-GGUF/resolve/main/phi-2-meditron.fp16.gguf) | fp16 | 5.56 GB  |
+| [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  |
+| [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  |
+| [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  |
+| [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  |
+| [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  |
+| [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  |
+
+
+
+## Original Model Card:
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+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.
+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)
+
+### Model Description
+
+- **Finetuned by:** [`Mohamad Alhajar`](https://www.linkedin.com/in/muhammet-alhajar/) 
+- **Language(s) (NLP):** English
+- **Finetuned from model:** [`microsoft/phi-2`](https://huggingface.co/microsoft/phi-2)
+
+### Prompt Template
+```
+### Instruction:
+
+<prompt> (without the <>)
+
+### Response:
+```
+
+
+## How to Get Started with the Model
+
+Use the code sample provided in the original post to interact with the model.
+```python
+from transformers import AutoTokenizer,AutoModelForCausalLM
+ 
+model_id = "malhajar/phi-2-meditron"
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
+                                             device_map="auto",
+                                             torch_dtype=torch.float16,
+                                              trust_remote_code= True,
+                                             revision="main")
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+question: "what is tract infection?"
+# For generating a response
+prompt = '''
+### Instruction:
+{question} 
+
+### Response:'''
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+output = model.generate(inputs=input_ids,max_new_tokens=512,pad_token_id=tokenizer.eos_token_id,top_k=50, do_sample=True,
+        top_p=0.95)
+response = tokenizer.decode(output[0])
+
+print(response)
+```