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malhajar/meditron-7b-chat
meditron-7b-chat is a text generation model from malhajar. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as llama2.
meditron-7b-chat is a finetuned version of epfl-llm/meditron-7b using SFT Training on the Alpaca Dataset. This model can answer information about different excplicit ideas in medicine (see epfl-llm/meditron-7b for mor…
Downloads · 30 days
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From the Hugging Face model README
meditron-7b-chat is a finetuned version of epfl-llm/meditron-7b using SFT Training on the Alpaca Dataset.
This model can answer information about different excplicit ideas in medicine (see epfl-llm/meditron-7b for more info)
Mohamad Alhajarepfl-llm/meditron-7b### Instruction:
<prompt> (without the <>)
### Response:
Use the code sample provided in the original post to interact with the model.
from transformers import AutoTokenizer,AutoModelForCausalLM
model_id = "malhajar/meditron-7b-chat"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
torch_dtype=torch.float16,
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)
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 49.59 |
| AI2 Reasoning Challenge (25-Shot) | 50.77 |
| HellaSwag (10-Shot) | 75.37 |
| MMLU (5-Shot) | 40.49 |
| TruthfulQA (0-shot) | 48.56 |
| Winogrande (5-shot) | 73.16 |
| GSM8k (5-shot) | 9.17 |