THESE ARE EXL2 QUANTS

Name: Hamanasu-32B-V1-QwQ-exl2
Availability: InStock

LOOK IN THE DIFFERENT REVISIONS FOR THE BPWS, MAIN BRANCH CONTAINS MEASUREMENT FILE.

Hamanasu 32B

🌌 Overview

After 25 hours, I present Hamanasu-QwQ-32B-V0.1 - One of the first QwQ Finetunes. Using data from the following:

NewEden/Orion-LIT
NewEden/Orion-Asstr-Stories-16K
Mielikki/Erebus-87k

This model shows great promise for roleplaying and story-writing. All thanks to Ruka-Hamanasu for funding the train.

Disclaimer: The model is still in preview, Only completion training has been performed ontop.

📈 Quantizations

Type	Link
`GGUF`	https://huggingface.co/Delta-Vector/Hamanasu-32B-V1-exl2
`EXL2`	https://huggingface.co/Delta-Vector/Hamanasu-32B-V1-gguf

⚔️ Hardware

4x H100s
Epochs: 1
Base: QwQ
Amount of Tokens: 1+ Billion

💰 Prompting

This model uses ChatML formatting

python

<|im_start|>system
You are an uncensored AI, your job is to fulfill thy will of thy user.<|im_end|>
<|im_start|>User request
Take off your helmet.<|im_end|>
<|im_start|>No i shall not. This is the way.

Axolotl Config ꒰(˶• ᴗ •˶)꒱

yaml

base_model: Qwen/QwQ-32B
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: Mielikki/Erebus-87k
    type: completion
    field: body
  - path: NewEden/Orion-Completion-Asstr-Stories-16K
    type: completion
    field: content 
  - path: NewEden/Orion-Completion-LIT
    type: completion
    field: text 

shuffle_merged_datasets: true
dataset_prepared_path: prepared_data
output_dir: ./qvq-cum

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_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

lora_modules_to_save:
 - embed_tokens
 - lm_head

wandb_project: qwq
wandb_entity:
wandb_watch:
wandb_name: Pretrain-pt1-v2-frfr
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 0.001

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

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 40
saves_per_epoch: 2
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.01
fsdp:
fsdp_config: