malhajar/phi-2-meditron-GGUF
Quantized GGUF model files for phi-2-meditron from malhajar
Name | Quant method | Size |
---|---|---|
phi-2-meditron.fp16.gguf | fp16 | 5.56 GB |
phi-2-meditron.q2_k.gguf | q2_k | 1.17 GB |
phi-2-meditron.q3_k_m.gguf | q3_k_m | 1.48 GB |
phi-2-meditron.q4_k_m.gguf | q4_k_m | 1.79 GB |
phi-2-meditron.q5_k_m.gguf | q5_k_m | 2.07 GB |
phi-2-meditron.q6_k.gguf | q6_k | 2.29 GB |
phi-2-meditron.q8_0.gguf | q8_0 | 2.96 GB |
Original Model Card:
Model Card for Model ID
phi-2-meditron is a finetuned version of 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
for more info)
Model Description
- Finetuned by:
Mohamad Alhajar
- Language(s) (NLP): English
- Finetuned from model:
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.
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)
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