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Vishykm
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Model Details
Model Provider
Vishykm
Model Tree
mistralai/Mixtral-8x7B-Instruct-v0.1
Input Modalities
Output Modalities
Supported Functionality
| Metric | base | adapted |
|---|---|---|
| Win rate (your dataset) | 36 | 64 |
| Win rate (Personal Finance category) | 21 | 80 |
| Parameter | Value |
|---|---|
| lora_r | 64 |
| lora_alpha | 128 |
| lora_dropout | 0 |
| target modules | q_proj, k_proj, v_proj, o_proj |
| trainable modules | all-linear |
| task type | CAUSAL_LM |
| Parameter | Value |
|---|---|
| n_epochs | 5 |
| batch_size | max |
| learning_rate | 0.0002 |
| lr_scheduler_type | cosine |
| scheduler_num_cycles | 0.5 |
| min_lr_ratio | 0.1 |
| warmup_ratio | 0.03 |
| weight_decay | 0.01 |
| max_grad_norm | 1 |
| train_on_inputs | false |
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base = "mistralai/Mixtral-8x7B-Instruct-v0.1"adapter = "Vishykm/adaption_indian_finance_dataset" tokenizer = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")model = PeftModel.from_pretrained(model, adapter)from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base = "mistralai/Mixtral-8x7B-Instruct-v0.1"adapter = "Vishykm/adaption_indian_finance_dataset" tokenizer = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")model = PeftModel.from_pretrained(model, adapter)