language
license
tags
datasets
base_model
model-index
llama2
epfl-llm/meditron-7b
name
results
meditron-7b-chat
task
dataset
metrics
source
type
name
text-generation
Text Generation
name
type
config
split
args
AI2 Reasoning Challenge (25-Shot)
ai2_arc
ARC-Challenge
test
type
value
name
acc_norm
50.77
normalized accuracy
url
name
Open LLM Leaderboard
task
dataset
metrics
source
type
name
text-generation
Text Generation
name
type
split
args
HellaSwag (10-Shot)
hellaswag
validation
type
value
name
acc_norm
75.37
normalized accuracy
url
name
Open LLM Leaderboard
task
dataset
metrics
source
type
name
text-generation
Text Generation
name
type
config
split
args
MMLU (5-Shot)
cais/mmlu
all
test
type
value
name
acc
40.49
accuracy
url
name
Open LLM Leaderboard
task
dataset
metrics
source
type
name
text-generation
Text Generation
name
type
config
split
args
TruthfulQA (0-shot)
truthful_qa
multiple_choice
validation
url
name
Open LLM Leaderboard
task
dataset
metrics
source
type
name
text-generation
Text Generation
name
type
config
split
args
Winogrande (5-shot)
winogrande
winogrande_xl
validation
type
value
name
acc
73.16
accuracy
url
name
Open LLM Leaderboard
task
dataset
metrics
source
type
name
text-generation
Text Generation
name
type
config
split
args
GSM8k (5-shot)
gsm8k
main
test
type
value
name
acc
9.17
accuracy
url
name
Open LLM Leaderboard
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)
### 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/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