README.md

---
library_name: transformers
license: llama3
base_model:
- nbeerbower/llama-3-Stheno-Mahou-8B
datasets:
- flammenai/FlameMix-DPO-v1
- flammenai/Grill-preprod-v1_chatML
- flammenai/Grill-preprod-v2_chatML
---
**Exllamav2** quant (**exl2** / **4.0 bpw**) made with ExLlamaV2 v0.0.21

Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_2bpw_exl2)**</center> | <center>3250 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_5bpw_exl2)**</center> | <center>3479 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_0bpw_exl2)**</center> | <center>3893 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_5bpw_exl2)**</center> | <center>4311 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_75bpw_exl2)**</center> | <center>4518 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_0bpw_exl2)**</center> | <center>4727 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_25bpw_exl2)**</center> | <center>4935 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-5_0bpw_exl2)**</center> | <center>5557 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_0bpw_exl2)**</center> | <center>6496 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_5bpw_exl2)**</center> | <center>6902 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-8_0bpw_exl2)**</center> | <center>8131 MB</center> | <center>8</center> |

![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png)

# Mahou-1.2a-llama3-8B

Mahou is our attempt to build a production-ready conversational/roleplay LLM.

Future versions will be released iteratively and finetuned from flammen.ai conversational data.

### Chat Format

This model has been trained to use ChatML format.

```
<|im_start|>system
{{system}}<|im_end|>
<|im_start|>{{char}}
{{message}}<|im_end|>
<|im_start|>{{user}}
{{message}}<|im_end|>
```

# Roleplay Format

- Speech without quotes.
- Actions in `*asterisks*`

```
*leans against wall cooly* so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass.
```

### ST Settings

1. Use ChatML for the Context Template.
2. Turn on Instruct Mode for ChatML.
3. Use the following stopping strings: `["<", "|", "<|", "\n"]`

### Method

Finetuned using an A100 on Google Colab.

[Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne)

### Configuration

LoRA, model, and training settings:

```python
# LoRA configuration
peft_config = LoraConfig(
    r=16,
    lora_alpha=16,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
)

# Model to fine-tune
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    load_in_4bit=True
)
model.config.use_cache = False

# Reference model
ref_model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    load_in_4bit=True
)

# Training arguments
training_args = TrainingArguments(
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    gradient_checkpointing=True,
    learning_rate=5e-5,
    lr_scheduler_type="cosine",
    max_steps=2000,
    save_strategy="no",
    logging_steps=1,
    output_dir=new_model,
    optim="paged_adamw_32bit",
    warmup_steps=100,
    bf16=True,
    report_to="wandb",
)

# Create DPO trainer
dpo_trainer = DPOTrainer(
    model,
    ref_model,
    args=training_args,
    train_dataset=dataset,
    tokenizer=tokenizer,
    peft_config=peft_config,
    beta=0.1,
    force_use_ref_model=True
)

# Fine-tune model with DPO
dpo_trainer.train()
```