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chatbotNZ
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mtext-150224-merged

PEFT
PyTorch
GGUF
llama
axolotl
Generated from Trainer
Model card Files
xet
 Community
How to use from
llama.cpp
Install (macOS, Linux) 
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf chatbotNZ/mtext-150224-merged:F16
# Run inference directly in the terminal:
llama cli -hf chatbotNZ/mtext-150224-merged:F16
Install from WinGet (Windows) 
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf chatbotNZ/mtext-150224-merged:F16
# Run inference directly in the terminal:
llama cli -hf chatbotNZ/mtext-150224-merged:F16
Use pre-built binary 
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf chatbotNZ/mtext-150224-merged:F16
# Run inference directly in the terminal:
./llama-cli -hf chatbotNZ/mtext-150224-merged:F16
Build from source code 
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf chatbotNZ/mtext-150224-merged:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chatbotNZ/mtext-150224-merged:F16
Use Docker 
docker model run hf.co/chatbotNZ/mtext-150224-merged:F16
Quick Links

Built with Axolotl

See axolotl config

axolotl version: 0.4.0

base_model: meta-llama/Llama-2-7b-hf
base_model_config: meta-llama/Llama-2-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: ascherrer/mtext-data-150224_2
    type: completion
    field: text
dataset_prepared_path: last_run_prepared
hub_model_id: ascherrer/mtext-150224_2
val_set_size: 0.01
output_dir: ./qlora-out

adapter: qlora
lora_model_dir:


sequence_len: 4096
sample_packing: true
eval_sample_packing: false

pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: "machine-de-textes"
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model: "checkpoint"

lora_modules_to_save:
 - embed_tokens
 - lm_head
 
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

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

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

warmup_steps: 10
eval_steps: 20
eval_table_size: 5
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
tokens: # these are delimiters
  - "<|s|>"
  - "<|e|>"

mtext-150224_2

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 2.0540

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: 0.0002
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • total_eval_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 10
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
1.8407 0.42 20 2.1162
1.6743 0.84 40 2.0768
1.5006 1.24 60 2.0654
1.5812 1.65 80 2.0598
1.5619 2.05 100 2.0535
1.5251 2.47 120 2.0537
1.5473 2.89 140 2.0540

Framework versions

  • PEFT 0.8.2
  • Transformers 4.38.0.dev0
  • Pytorch 2.1.2+cu118
  • Datasets 2.17.0
  • Tokenizers 0.15.0
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Architecture
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