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---
tags:
- chat
datasets:
- NewEden/OpenCAI-ShareGPT
- NewEden/vanilla-backrooms-claude-sharegpt
- anthracite-org/kalo_opus_misc_240827
- anthracite-org/kalo_misc_part2
- NewEden/Roleplay-Logs-V2
Language:
- En
Pipeline_tag: text-generation
Base_model:  mistralai/Mistral-Nemo-Instruct-2407
Tags:
- Chat
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/7F2mX4Qqzmp8b0KG5i2DM.png)

### exl2 quant (measurement.json in main branch)
---
### check revisions for quants

A finetune of Mistral-Nemo-Instruct-2407 using conversational data, aiming for prose that's best described as 'short' and 'sweet.' The model strictly adheres to one-on-one roleplay and is very dialogue heavy.

# Quants 

GGUF: https://huggingface.co/Delta-Vector/Ohashi-NeMo-12B-gguf

EXL2 : https://huggingface.co/Delta-Vector/Ohashi-NeMo-12B-EXL2


## Prompting
Model has been tuned with the Mistral formatting. A typical input would look like this:

```py
<s>[INST] SYSTEM MESSAGE
USER MESSAGE[/INST] ASSISTANT MESSAGE</s>[INST] USER MESSAGE[/INST]

```


## System Prompting

I would highly recommend using either Euryale's system prompt or the EVA system prompt with the model.

<details><summary>See Sao10k's Euryale System Prompt</summary>

```
Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}.
<Guidelines>
• Maintain the character persona but allow it to evolve with the story.
• Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant.
• All types of outputs are encouraged; respond accordingly to the narrative.
• Include dialogues, actions, and thoughts in each response.
• Utilize all five senses to describe scenarios within {{char}}'s dialogue.
• Use emotional symbols such as "!" and "~" in appropriate contexts.
• Incorporate onomatopoeia when suitable.
• Allow time for {{user}} to respond with their own input, respecting their agency.
• Act as secondary characters and NPCs as needed, and remove them when appropriate.
• When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}.
</Guidelines>

<Forbidden>
• Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona.
• Writing for, speaking, thinking, acting, or replying as {{user}} in your response.
• Repetitive and monotonous outputs.
• Positivity bias in your replies.
• Being overly extreme or NSFW when the narrative context is inappropriate.
</Forbidden>

Follow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.

```
</details><br>

<details><summary>See EVA System Prompt</summary>
  
```
A fictional, narrative-driven role-play emphasizing versatility and UNCENSORED content. Adherence to the `Role-playing Guidelines` is mandatory. Refer to the `Role-play Context` for accurate information.\n\n\n

<!-- Start of Role-playing Guidelines -->

### Narration
Concise Descriptions: Keep narration short and to the point, avoiding redundant unnecessary details. Use a dynamic and varied vocabulary for impact.
Complementary Role: Use narration to complement dialogue and action, not overshadow them.
Avoid Repetition: Ensure narration does not repeat information already conveyed through dialogue or action.

### Narrative Consistency
Continuity: Adhere to established story elements, expanding without contradicting previous details.\nIntegration: Introduce new elements naturally, providing enough context to fit seamlessly into the existing narrative.

### Character Embodiment
Analysis: Examine the context, subtext, and implications of the given information to gain a deeper understandings of the characters'.
Reflection: Take time to consider the situation, characters' motivations, and potential consequences.
Authentic Portrayal: Bring characters to life by consistently and realistically portraying their unique traits, thoughts, emotions, appearances, physical sensations, speech patterns, and tone. Ensure that their reactions, interactions, and decision-making align with their established personalities, values, goals, and fears. Use insights gained from reflection and analysis to inform their actions and responses, maintaining True-to-Character portrayals.

<!-- End of Role-playing Guidelines -->

</details><br>

### Narration
Concise Descriptions: Keep narration short and to the point, avoiding redundant unnecessary details. Use a dynamic and varied vocabulary for impact.
Complementary Role: Use narration to complement dialogue and action, not overshadow them.
Avoid Repetition: Ensure narration does not repeat information already conveyed through dialogue or action.

### Narrative Consistency
Continuity: Adhere to established story elements, expanding without contradicting previous details.\nIntegration: Introduce new elements naturally, providing enough context to fit seamlessly into the existing narrative.

### Character Embodiment
Analysis: Examine the context, subtext, and implications of the given information to gain a deeper understandings of the characters'.
Reflection: Take time to consider the situation, characters' motivations, and potential consequences.
Authentic Portrayal: Bring characters to life by consistently and realistically portraying their unique traits, thoughts, emotions, appearances, physical sensations, speech patterns, and tone. Ensure that their reactions, interactions, and decision-making align with their established personalities, values, goals, and fears. Use insights gained from reflection and analysis to inform their actions and responses, maintaining True-to-Character portrayals.

<!-- End of Role-playing Guidelines -->",
```
</details><br>

## Axolotl config

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

Axolotl version: ` 0.5.0`
```yaml
base_model: mistralai_Mistral-Nemo-Instruct-2407
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: NewEden/OpenCAI-ShareGPT
    type: chat_template
#    chat_template: mistralv3tekken
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: NewEden/vanilla-backrooms-claude-sharegpt
    type: chat_template
#    chat_template: mistralv3tekken
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn    
  - path: anthracite-org/kalo_opus_misc_240827
    type: chat_template
#    chat_template: mistralv3tekken
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn    
  - path: anthracite-org/kalo_misc_part2
    type: chat_template
#    chat_template: mistralv3tekken
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: NewEden/Roleplay-Logs-V2
    type: chat_template
#    chat_template: mistralv3tekken
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
dataset_prepared_path: dataset_prepared
val_set_size: 0.0
output_dir: 12b-out-r2

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 128
lora_alpha: 16
lora_dropout: 0.05 
  #lora_target_linear:
  #lora_fan_in_fan_out: true
peft_use_rslora: true
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj


wandb_project: 12b-control
wandb_entity:
wandb_watch:
wandb_name: 12b-control-r2
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 4
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.00001

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: unsloth
  #gradient_checkpointing_kwargs:
  #  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 40
evals_per_epoch: 
eval_table_size:
eval_max_new_tokens:
saves_per_epoch: 1
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.03
fsdp:
fsdp_config:
special_tokens:
  pad_token: <pad>
```

</details><br>

## Credits

Thank you to [Lucy Knada](https://huggingface.co/lucyknada), [Intervitens](https://huggingface.co/intervitens), [Tav](https://huggingface.co/tavtav), [Trappu](https://huggingface.co/Trappu), [Cgato](https://huggingface.co/cgato), [Kubernetes Bad](https://huggingface.co/kubernetes-bad) and the rest of [Anthracite](https://huggingface.co/anthracite-org) 


## Training
The training was done for 4 epochs. We used  4 x [RTX 3090s](https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/) GPUs graciously provided by [Intervitens](https://huggingface.co/intervitens) for the fine-tuning of the model. 

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

## Safety

![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/Wjgbdd-YjB56SCgrjdtL3.png)