---
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: []
---

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should probably proofread and complete it, then remove this comment. -->

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<details><summary>See axolotl config</summary>

axolotl version: `0.10.0.dev0`
```yaml
# ===================================================================
# 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>"

```

</details><br>

# combined_model-finetune

This model is a fine-tuned version of [mistralai/Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/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