|
--- |
|
tags: |
|
- chat |
|
- roleplay |
|
- storywriting |
|
- mistral |
|
- finetune |
|
datasets: |
|
- NewEden/Orion-Asstr-Stories-16K |
|
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned-20k |
|
Language: |
|
- En |
|
Pipeline_tag: text-generation |
|
Base_model: mistralai/Mistral-7B-v0.3 |
|
Tags: |
|
- Chat |
|
base_model: |
|
- Delta-Vector/Hamanasu-7B-Base |
|
pipeline_tag: text-generation |
|
library_name: transformers |
|
--- |
|
|
|
<img src="https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/LWMr-e3nh9vounB-1yV6F.png" alt="alt text" width="500"/> |
|
|
|
|
|
A finetune of Mistral-7B-V0.3 to test out the Orion-Asstr dataset, This model was completion trained with Orion Asstr using Unsloth and then instruct-tuned with Gryphe's 20K Sonnetorca subset. The model leans towards RP format *actions* "Dialogue" and shorter responses. |
|
|
|
# Quants |
|
|
|
GGUF : https://huggingface.co/Delta-Vector/Hamanasu-7B-instruct-gguf |
|
|
|
EXL2 : https://huggingface.co/Delta-Vector/Hamanasu-7B-instruct-exl2 |
|
|
|
|
|
## Prompting |
|
Model has been tuned with the Mistral formatting. A typical input would look like this: |
|
|
|
```py |
|
"""<s> [INST] Hello, how are you? [/INST] I'm doing great. How can I help you today?</s> [INST] I'd like to show off how chat templating works! [/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> |
|
|
|
## Unsloth config |
|
|
|
<details><summary>See Unsloth SFT Trainer config</summary> |
|
|
|
```py |
|
from unsloth import FastLanguageModel |
|
import torch |
|
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally! |
|
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+ |
|
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False. |
|
|
|
# 4bit pre quantized models we support for 4x faster downloading + no OOMs. |
|
fourbit_models = [ |
|
"unsloth/mistral-7b-bnb-4bit", |
|
"unsloth/mistral-7b-instruct-v0.2-bnb-4bit", |
|
"unsloth/llama-2-7b-bnb-4bit", |
|
"unsloth/llama-2-13b-bnb-4bit", |
|
"unsloth/codellama-34b-bnb-4bit", |
|
"unsloth/tinyllama-bnb-4bit", |
|
] # More models at https://huggingface.co/unsloth |
|
|
|
model, tokenizer = FastLanguageModel.from_pretrained( |
|
model_name = "Delta-Vector/Hamanasu-7B-Base, # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B |
|
max_seq_length = max_seq_length, |
|
dtype = dtype, |
|
load_in_4bit = load_in_4bit, |
|
# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf |
|
) |
|
|
|
"""We now add LoRA adapters so we only need to update 1 to 10% of all parameters!""" |
|
|
|
model = FastLanguageModel.get_peft_model( |
|
model, |
|
r = 64, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128 |
|
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", |
|
"gate_proj", "up_proj", "down_proj",], |
|
lora_alpha = 32, |
|
lora_dropout = 0, # Supports any, but = 0 is optimized |
|
bias = "none", # Supports any, but = "none" is optimized |
|
use_gradient_checkpointing = True, |
|
random_state = 3407, |
|
use_rslora = True, # We support rank stabilized LoRA |
|
loftq_config = None, # And LoftQ |
|
) |
|
|
|
|
|
from unsloth.chat_templates import get_chat_template |
|
|
|
tokenizer = get_chat_template( |
|
tokenizer, |
|
chat_template = "mistral", # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth |
|
mapping = {"role" : "from", "content" : "value", "user" : "human", "assistant" : "gpt"}, # ShareGPT style |
|
map_eos_token = True, # Maps <|im_end|> to </s> instead |
|
) |
|
|
|
def formatting_prompts_func(examples): |
|
convos = examples["conversations"] |
|
texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos] |
|
return { "text" : texts, } |
|
pass |
|
|
|
from datasets import load_dataset |
|
dataset = load_dataset("anthracite-org/kalo-opus-instruct-22k-no-refusal", split = "train") |
|
dataset = dataset.map(formatting_prompts_func, batched = True,) |
|
|
|
|
|
from trl import SFTTrainer |
|
from transformers import TrainingArguments |
|
|
|
trainer = SFTTrainer( |
|
model = model, |
|
tokenizer = tokenizer, |
|
train_dataset = dataset, |
|
dataset_text_field = "text", |
|
max_seq_length = max_seq_length, |
|
dataset_num_proc = 2, |
|
packing = False, # Can make training 5x faster for short sequences. |
|
args = TrainingArguments( |
|
per_device_train_batch_size = 2, |
|
gradient_accumulation_steps = 8, |
|
warmup_steps = 25, |
|
num_train_epochs=2, |
|
learning_rate = 2e-5, |
|
fp16 = not torch.cuda.is_bf16_supported(), |
|
bf16 = torch.cuda.is_bf16_supported(), |
|
logging_steps = 1, |
|
optim = "paged_adamw_8bit", |
|
weight_decay = 0.01, |
|
lr_scheduler_type = "linear", |
|
seed = 3407, |
|
output_dir = "outputs", |
|
report_to = "wandb", # Use this for WandB etc |
|
), |
|
) |
|
|
|
#@title Show current memory stats |
|
gpu_stats = torch.cuda.get_device_properties(0) |
|
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3) |
|
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3) |
|
print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.") |
|
print(f"{start_gpu_memory} GB of memory reserved.") |
|
|
|
trainer_stats = trainer.train() |
|
|
|
#@title Show final memory and time stats |
|
used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3) |
|
used_memory_for_lora = round(used_memory - start_gpu_memory, 3) |
|
used_percentage = round(used_memory /max_memory*100, 3) |
|
lora_percentage = round(used_memory_for_lora/max_memory*100, 3) |
|
print(f"{trainer_stats.metrics['train_runtime']} seconds used for training.") |
|
print(f"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.") |
|
print(f"Peak reserved memory = {used_memory} GB.") |
|
print(f"Peak reserved memory for training = {used_memory_for_lora} GB.") |
|
print(f"Peak reserved memory % of max memory = {used_percentage} %.") |
|
print(f"Peak reserved memory for training % of max memory = {lora_percentage} %.") |
|
``` |
|
|
|
</details><br> |
|
|
|
## Credits |
|
|
|
Thank you to [Lucy Knada](https://huggingface.co/lucyknada), [jeiku](https://huggingface.co/jeiku), [Intervitens](https://huggingface.co/intervitens), [Kalomaze](https://huggingface.co/kalomaze), [Kubernetes Bad](https://huggingface.co/kubernetes-bad) and the rest of [Anthracite](https://huggingface.co/anthracite-org) |
|
|
|
|
|
## Training |
|
The training was done for 2 epochs. We used 1 x RTX A4000 |
|
|
|
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made%20with%20unsloth.png" alt="Made with Unsloth" width="200" height="32"/>](https://github.com/unslothai/unsloth) |
|
|
|
## Safety |
|
|
|
Nein. |
