license, library_name, datasets, base_model, model-index
| license |
library_name |
datasets |
base_model |
model-index |
| apache-2.0 |
transformers |
| andysalerno/ansalern-nectar-inputoutput |
|
mistralai/Mistral-7B-v0.1 |
| name |
results |
| mistral-sft-v3 |
| 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 |
61.35 |
normalized accuracy |
|
|
|
|
| task |
dataset |
metrics |
source |
| type |
name |
| text-generation |
Text Generation |
|
| name |
type |
split |
args |
| HellaSwag (10-Shot) |
hellaswag |
validation |
|
|
| type |
value |
name |
| acc_norm |
82.23 |
normalized accuracy |
|
|
|
|
| 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 |
63.4 |
accuracy |
|
|
|
|
| task |
dataset |
metrics |
source |
| type |
name |
| text-generation |
Text Generation |
|
| name |
type |
config |
split |
args |
| TruthfulQA (0-shot) |
truthful_qa |
multiple_choice |
validation |
|
|
|
|
|
| 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 |
77.66 |
accuracy |
|
|
|
|
| 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 |
32.45 |
accuracy |
|
|
|
|
|
|
|
This is mistralai/Mistral-7B-v0.1, but with the special tokens added for ChatML, and then lightly finetuned with sft using a ChatML formatted dataset: andysalerno/ansalern-nectar-inputoutput
The training was very light, so while this model correctly follows ChatML formatting, it is not intended to be a chat model.
Rather, it is intended to be a base for further fine-tuning models that will use ChatML.
Detailed results can be found here
| Metric |
Value |
| Avg. |
60.93 |
| AI2 Reasoning Challenge (25-Shot) |
61.35 |
| HellaSwag (10-Shot) |
82.23 |
| MMLU (5-Shot) |
63.40 |
| TruthfulQA (0-shot) |
48.49 |
| Winogrande (5-shot) |
77.66 |
| GSM8k (5-shot) |
32.45 |