Fewshot-Metamath-OrcaVicuna-Mistral/README.md

license, base_model, datasets

license	base_model	datasets
apache-2.0	mistralai/Mistral-7B-v0.1	abacusai/MetaMathFewshot shahules786/orca-chat anon8231489123/ShareGPT_Vicuna_unfiltered

This model was trained on our MetamathFewshot dataset, as well as the Vicuna dataset and the OrcaChat dataset.

It has been finetuned from base Mistral 7B

Usage

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:

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.