Delta-Vector/Holland-4B-V1

A model made to continue off my previous work on Magnum 4B, A small model made for creative writing / General assistant tasks, finetuned ontop of IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml, this model is made to be more coherent and generally be better then the 4B at both writing and assistant tasks.

Quants

GGUF: https://huggingface.co/NewEden/Holland-4B-gguf

EXL2: https://huggingface.co/NewEden/Holland-4B-exl2

Prompting

Model has been Instruct tuned with the ChatML formatting. A typical input would look like this:

"""<|im_start|>system
system prompt<|im_end|>
<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant
"""

Support

No longer needed as LCPP has merged support - just update.

To run inference on this model, you'll need to use Aphrodite, vLLM or EXL 2/tabbyAPI, as llama.cpp hasn't yet merged the required pull request to fix the llama 3.1 rope_freqs issue with custom head dimensions.

However, you can work around this by quantizing the model yourself to create a functional GGUF file. Note that until this PR is merged, the context will be limited to 8 k tokens.

To create a working GGUF file, make the following adjustments:

1. Remove the "rope_scaling": {} entry from config.json
2. Change "max_position_embeddings" to 8192 in config.json

These modifications should allow you to use the model with llama. Cpp, albeit with the mentioned context limitation.

Axolotl config

See axolotl config

Axolotl version: 0.4.1

base_model: IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: NewEden/Gryphe-3.5-16k-Subset
    type: sharegpt
    conversation: chatml
  - path: Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
    type: sharegpt
    conversation: chatml
  - path: anthracite-org/kalo-opus-instruct-22k-no-refusal
    type: sharegpt
    conversation: chatml
  - path: PJMixers/lodrick-the-lafted_OpusStories-ShareGPT
    type: sharegpt
    conversation: chatml

chat_template: chatml

val_set_size: 0.01
output_dir: ./outputs/out

adapter:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:

sequence_len: 16384
# sequence_len: 32768
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

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

wandb_project: 
wandb_entity:
wandb_watch:
wandb_name: 
wandb_log_model:

gradient_accumulation_steps: 32
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
#optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.00002
weight_decay: 0.05

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

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

warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1

debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json
#deepspeed:
fsdp:
fsdp_config:

special_tokens:
  pad_token: <|finetune_right_pad_id|>

Credits

• anthracite-org/kalo-opus-instruct-22k-no-refusal
• NewEden/Gryphe-3.5-16k-Subset
• Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
• lodrick-the-lafted/OpusStories

Training

The training was done for 2 epochs. We used 2 x RTX 6000s GPUs graciously provided by Kubernetes_Bad for the full-parameter fine-tuning of the model.