Text Generation
PEFT
Safetensors
English
code
cobol
code-generation
mainframe-modernization
lora
sft
trl
unsloth
ministral
conversational
Eval Results (legacy)
Instructions to use axeltta/mistral-axel-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use axeltta/mistral-axel-1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/ministral-3-8b-instruct-2512-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "axeltta/mistral-axel-1") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
File size: 464 Bytes
9e7fd8a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"model": "unsloth/Ministral-3-8B-Instruct-2512",
"lora_r": 16,
"lora_alpha": 16,
"max_steps": 40,
"learning_rate": 0.0001,
"batch_size": 4,
"seed": 12627998,
"train_n": 95,
"val_n": 10,
"eval_metrics": {
"eval_loss": NaN,
"eval_runtime": 5.3084,
"eval_samples_per_second": 1.884,
"eval_steps_per_second": 0.565,
"epoch": 1.6666666666666665
},
"train_runtime_s": 65.463,
"adapter_path": "/adapters/cobol-string-sft"
} |