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neovim-help-adapter
aegis-nvim/neovim-help-adapter
neovim-help-adapter is a machine learning model from aegis-nvim. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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.safetensors9 MB · 45%
At a glance
- Library
- peft
- License
- apache-2.0
- Model type
- llama
- Access
- Public
- Created
- May 3, 2025
- Updated
- May 3, 2025
- SHA
- 765b17c0
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
<details><summary>See axolotl config</summary>
axolotl version: 0.8.0.dev0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
trust_remote_code: true
load_in_8bit: true
bf16: true
adapter: lora
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
- q_proj
- v_proj
pretraining_dataset:
- name: default
path: aegis-nvim/neovim-help
type: pretrain
text_column: text
trust_remote_code: false
val_set_size: 0
sequence_len: 2048
group_by_length: false
max_steps: 5000
micro_batch_size: 16
gradient_accumulation_steps: 2
gradient_checkpointing: true
optimizer: adamw_bnb_8bit
learning_rate: 0.0001
lr_scheduler: cosine
warmup_ratio: 0.03
max_grad_norm: 1.0
output_dir: ./outputs/dapt-lora
save_strategy: steps
save_steps: 5000
save_total_limit: 1
save_safetensors: true
outputs/dapt-lora
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 150
- training_steps: 5000
Training results
Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
- Library
- peft
- Type
- llama
- License
- apache-2.0
- Created
- May 3, 2025
- Updated
- May 3, 2025