This model uses a specific prompt format which is encoded as a chat template. To apply this, you can use the tokenizer.apply_chat_template() method of the attached tokenizer:

python

messages = [
    {"role": "user", "content": "What is the capital of Spain?"},
    {"role": "assistant", "content": "The capital of Spain is Madrid."}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)

Evaluation Results

HuggingFace Leaderboard

Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
67.33	59.64	81.82	61.69	53.23	78.45	69.14

For comparison the GSM8K score for the original metamath/MetaMath-Mistral-7B was 68.84 and average score was 65.78.

MT-Bench

Turn 1	Turn 2	Average
6.90	6.52	6.71

Training Details

Instruction tuned with the following parameters:

LORA, Rank 8, Alpha 16, Dropout 0.05, all modules (QKV and MLP)
3 epochs
Micro Batch Size 32 over 4xH100, gradient accumulation steps = 1
AdamW with learning rate 5e-5

Bias, Risks, and Limitations

The model has not been evaluated for safety and is only intended for research and experiments.

abacusai/Fewshot-Metamath-OrcaVicuna-Mistral

Usage

Evaluation Results

HuggingFace Leaderboard

MT-Bench

Training Details

Bias, Risks, and Limitations

abacusai/
Fewshot-Metamath-OrcaVicuna-Mistral