# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("drisspg/mathew_train_v1")
model = AutoModelForCausalLM.from_pretrained("drisspg/mathew_train_v1")Quick Links
out/Mistral-7B-sft-v1
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9216
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.0308 | 0.1 | 20 | 0.9749 |
| 0.9065 | 0.2 | 40 | 0.9535 |
| 0.9799 | 0.3 | 60 | 0.9446 |
| 1.2045 | 0.4 | 80 | 0.9390 |
| 0.9185 | 0.5 | 100 | 0.9332 |
| 0.9541 | 0.6 | 120 | 0.9282 |
| 1.0332 | 0.69 | 140 | 0.9252 |
| 1.0345 | 0.79 | 160 | 0.9229 |
| 1.0117 | 0.89 | 180 | 0.9217 |
| 1.0495 | 0.99 | 200 | 0.9216 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.7
- Tokenizers 0.14.1
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Model tree for drisspg/mathew_train_v1
Base model
mistralai/Mistral-7B-v0.1
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="drisspg/mathew_train_v1")