metadata
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
tags:
- generated_from_trainer
datasets:
- combined_dataset.jsonl
model-index:
- name: combined_model-finetune
results: []
See axolotl config
axolotl version: 0.10.0.dev0
# ===================================================================
# CONFIG: For a single, combined "Conversion & Debug" Model
# Using the stable 'alpaca' format.
# ===================================================================
# --- Core Model Configuration (Kept as requested) ---
base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# --- Performance, Quality, and Memory Optimization ---
flash_attention: true
load_in_4bit: true
load_in_8bit: false
adapter: lora
# --- Dataset Configuration (KEY CHANGE) ---
# Reverted to the stable 'alpaca' type.
# Axolotl will automatically look for 'instruction', 'input', 'output' fields.
datasets:
- path: combined_dataset.jsonl # This is your new, flattened dataset
type: alpaca
# --- Output Directory ---
output_dir: ./combined_model-finetune
# --- Training Hyperparameters ---
sequence_len: 2048
micro_batch_size: 1
gradient_accumulation_steps: 4
num_epochs: 3
learning_rate: 3e-5
# --- LoRA Configuration ---
lora_r: 16
lora_alpha: 32
lora_dropout: 0.15
lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
# --- Logging, Evaluation, and Saving (Kept as requested) ---
logging_steps: 2
evaluation_strategy: "steps"
eval_steps: 2
save_strategy: "steps"
save_steps: 9999
val_set_size: 0.05
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
combined_model-finetune
This model is a fine-tuned version of mistralai/Mistral-Small-3.1-24B-Instruct-2503 on the combined_dataset.jsonl dataset. It achieves the following results on the evaluation set:
- Loss: 0.1924
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: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- training_steps: 21
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.1481 | 1 | 0.2836 |
| 0.2664 | 0.2963 | 2 | 0.2538 |
| 0.4832 | 0.5926 | 4 | 0.2200 |
| 0.3229 | 0.8889 | 6 | 0.2090 |
| 0.1517 | 1.1481 | 8 | 0.2022 |
| 0.3353 | 1.4444 | 10 | 0.2009 |
| 0.2418 | 1.7407 | 12 | 0.1958 |
| 0.0811 | 2.0 | 14 | 0.1942 |
| 0.0496 | 2.2963 | 16 | 0.1933 |
| 0.1906 | 2.5926 | 18 | 0.1927 |
| 0.3171 | 2.8889 | 20 | 0.1924 |
Framework versions
- PEFT 0.15.2
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1