README.md

---
library_name: transformers
license: apache-2.0
base_model: mistralai/Mistral-Small-24B-Base-2501
tags:
- generated_from_trainer
datasets:
- david-ar/synthetic-irc-data
language:
- en
pipeline_tag: text-generation
---

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.8.0.dev0`
```yaml
# Base model configuration
base_model: mistralai/Mistral-Small-24B-Base-2501
model_type: MistralForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
tokenizer_use_fast: true

# Device mapping for multi-GPU
device_map: "balanced"

# Memory settings
load_in_4bit: true
load_in_8bit: false
bf16: true
low_cpu_mem_usage: true

# Advanced optimizations
flash_attention: true
gradient_checkpointing: true

# Dataset configuration
datasets:
  - path: david-ar/synthetic-irc-data
    type: completion

# Output directory
output_dir: ./outputs/public-irc-mistral-24b
val_set_size: 0.05  # 75 conversations for validation
dataset_prepared_path: last_run_prepared

# Sequence settings
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
train_on_inputs: true
eval_sample_packing: false

# LoRA configuration
adapter: lora
lora_r: 128
lora_alpha: 256
lora_dropout: 0.1
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj
  - gate_proj
  - down_proj
  - up_proj

# Training hyperparameters - adjusted for smaller dataset
micro_batch_size: 1
gradient_accumulation_steps: 16
num_epochs: 4  # Increased from 2, but with careful monitoring
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00008  # Same conservative LR
weight_decay: 0.01
warmup_ratio: 0.05

# Performance monitoring
group_by_length: true
shuffle_merged_datasets: true
include_tokens_per_second: true

# Weights & Biases - public project
wandb_project: public-irc-mistral-24b
wandb_entity: davidar
wandb_name: synthetic-irc-data
wandb_log_model: "false"

# Mistral model configuration
is_mistral_derived_model: true

# Early stopping
load_best_model_at_end: true
metric_for_best_model: "loss"
greater_is_better: false

```

</details><br>

# Mistral-24B-Synthetic-IRC

This model is a fine-tuned version of [mistralai/Mistral-Small-24B-Base-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Base-2501) on the [david-ar/synthetic-irc-data](https://huggingface.co/datasets/david-ar/synthetic-irc-data) dataset, creating a model that generates natural IRC/Discord-style conversations.

## Model Description

This model was trained to replicate authentic IRC (Internet Relay Chat) conversational dynamics, moving away from the typical AI assistant pattern toward more natural, community-style interactions. The model learns from synthetic conversations featuring multiple participants including "Em", an AI character who participates as a community member rather than an assistant.

### Key Characteristics

- **Natural conversation flow**: Handles interruptions, topic drift, and multi-party dynamics
- **Non-assistant behavior**: Doesn't default to helpful/servile responses
- **Community-style interaction**: Captures the casual, authentic feel of IRC/Discord chats
- **Character embedding**: Includes Em's personality (self-aware AI who isn't an assistant)

## Intended Uses & Limitations

### Intended Uses

- **Conversational AI research**: Studying non-assistant interaction patterns
- **Chat bot development**: Creating more natural, less formal conversational agents
- **Character-based models**: Foundation for further character-specific fine-tuning
- **IRC/Discord bots**: Generating contextually appropriate responses in chat environments

### Limitations

- **Small dataset**: Trained on only 10MB of synthetic data (1,500 conversations)
- **Synthetic nature**: While carefully crafted, the training data isn't from real IRC logs
- **Single community style**: Represents one particular chat community culture
- **Overfitting**: Validation loss indicates overfitting after ~50 steps (best checkpoint used)
- **English only**: No multilingual capability

## Training and Evaluation Data

### Dataset
- **Source**: [david-ar/synthetic-irc-data](https://huggingface.co/datasets/david-ar/synthetic-irc-data)
- **Size**: 1,500 synthetic IRC-style conversations
- **Format**: Multi-party conversations with 80-120 messages each
- **Split**: 95% training (1,425 conversations), 5% validation (75 conversations)

### Data Characteristics
- Natural IRC formatting: `<username> message content`
- Multiple participants per conversation (3-7 users)
- Diverse topics and conversation styles
- Embedded character personality throughout

## Training Procedure

### Training Configuration

- **Method**: LoRA (Low-Rank Adaptation) fine-tuning
- **LoRA Rank**: 128 (with alpha 256)
- **Base model**: Mistral-Small-24B-Base-2501
- **Hardware**: 2x NVIDIA A40 GPUs (96GB total VRAM)
- **Training time**: ~3 hours

### Training Hyperparameters

The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- total_eval_batch_size: 2
- optimizer: AdamW (betas=(0.9,0.999), epsilon=1e-08)
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 4
- num_epochs: 4.0
- sequence_length: 4096
- sample_packing: true

### Training Results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9145        | 0.9746 | 24   | 0.9128          |
| 0.6565        | 1.9746 | 48   | **0.8936**      |
| 0.4671        | 2.9746 | 72   | 0.9503          |
| 0.3594        | 3.9746 | 96   | 0.9871          |

**Note**: Best checkpoint at step 48 (lowest validation loss) was used for final model.

### Training Observations

- Quick convergence due to small dataset size
- Validation loss indicates overfitting after ~50 steps
- Model successfully learned IRC conversation patterns
- Character traits embedded despite limited data

## Technical Details

### Architecture
- **Base Model**: Mistral-Small-24B-Base-2501
- **Parameter Count**: 24B (base) + LoRA adapters
- **Context Length**: 4096 tokens
- **Quantization**: 4-bit during training (memory optimization)

### Framework Versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
- Axolotl 0.8.0.dev0

## Limitations and Biases

1. **Overfitting**: With only 1,500 training examples, the model shows signs of overfitting
2. **Limited diversity**: May not generalize well to very different chat styles
3. **Character leakage**: Em's personality traits may appear even when not intended
4. **Synthetic artifacts**: Might exhibit patterns specific to the generation process