openhermes-gemma-2b-it
openhermes-gemma-2b-it is a variant of the Gemma 2B language model, which has been further fine-tuned on the OpenHermes-2.5 preference dataset using QLoRA. This fine-tuning process enhances the model's ability to understand and generate responses that align with user preferences in conversational settings.
Usage
Chat Template
The instruction-tuned models use a chat template that must be adhered to for conversational use. The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "Syed-Hasan-8503/openhermes-gemma-2b-it"
dtype = torch.bfloat16
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda",
torch_dtype=dtype,
)
chat = [{ "role": "user", "content": "What is Machine Learning?" }]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
After the prompt is ready, generation can be performed like this:
inputs = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=250)
print(tokenizer.decode(outputs[0]))
Inputs and outputs
- Input: Text string, such as a question, a prompt, or a document to be summarized.
- Output: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document.
Evaluation data
π Evaluation
AGIEVAL
Task | Version | Metric | Value | StdErr | |
---|---|---|---|---|---|
agieval_aqua_rat | 0 | acc | 24.02 | _ | 2.69 |
agieval_aqua_rat | 0 | acc_norm | 24.02 | _ | 2.69 |
agieval_logiqa_en | 0 | acc | 23.20 | _ | 1.66 |
agieval_logiqa_en | 0 | acc_norm | 24.42 | _ | 1.69 |
agieval_lsat_ar | 0 | acc | 18.26 | _ | 2.55 |
agieval_lsat_ar | 0 | acc_norm | 18.70 | _ | 2.58 |
agieval_lsat_lr | 0 | acc | 22.35 | _ | 1.85 |
agieval_lsat_lr | 0 | acc_norm | 23.53 | _ | 1.88 |
agieval_lsat_rc | 0 | acc | 20.82 | _ | 2.48 |
agieval_lsat_rc | 0 | acc_norm | 20.07 | _ | 2.45 |
agieval_sat_en | 0 | acc | 32.52 | _ | 3.27 |
agieval_sat_en | 0 | acc_norm | 32.52 | _ | 3.27 |
agieval_sat_en_without_passage | 0 | acc | 25.73 | _ | 3.05 |
agieval_sat_en_without_passage | 0 | acc_norm | 24.27 | _ | 2.99 |
agieval_sat_math | 0 | acc | 25.00 | _ | 2.93 |
agieval_sat_math | 0 | acc_norm | 20.91 | _ | 2.75 |
Average: 23.8
GPT4ALL
Task | Version | Metric | Value | StdErr | |
---|---|---|---|---|---|
arc_challenge | 0 | acc | 21.77 | _ | 1.21 |
arc_challenge | 0 | acc_norm | 24.15 | _ | 1.25 |
arc_easy | 0 | acc | 37.37 | _ | 0.99 |
arc_easy | 0 | acc_norm | 36.95 | _ | 0.99 |
boolq | 1 | acc | 65.60 | _ | 0.83 |
hellaswag | 0 | acc | 34.54 | _ | 0.47 |
hellaswag | 0 | acc_norm | 40.54 | _ | 0.49 |
openbookqa | 0 | acc | 15.00 | _ | 1.59 |
openbookqa | 0 | acc_norm | 27.40 | _ | 2.00 |
piqa | 0 | acc | 60.88 | _ | 1.14 |
piqa | 0 | acc_norm | 60.55 | _ | 1.14 |
winogrande | 0 | acc | 50.91 | _ | 1.41 |
Average: 39.9
BIGBENCH
Task | Version | Metric | Value | Std Err |
---|---|---|---|---|
bigbench_causal_judgement | 0 | MCG | 50 | 2.26 |
bigbench_date_understanding | 0 | MCG | 49.14 | 2.18 |
bigbench_disambiguation_qa | 0 | MCG | 49.31 | 2.74 |
bigbench_geometric_shapes | 0 | MCG | 14.18 | 1.37 |
bigbench_logical_deduction_5objs | 0 | MCG | 49.41 | 2.73 |
bigbench_logical_deduction_7objs | 0 | MCG | 41.48 | 2.46 |
bigbench_logical_deduction_3objs | 0 | MCG | 69.33 | 2.75 |
bigbench_movie_recommendation | 0 | MCG | 51.71 | 2.25 |
bigbench_navigate | 0 | MCG | 50 | 1.58 |
bigbench_reasoning_colored_obj | 0 | MCG | 51.92 | 0.99 |
bigbench_ruin_names | 0 | MCG | 48.14 | 2.01 |
bigbench_salient_trans_err_detec | 0 | MCG | 39.92 | 1.2 |
bigbench_snarks | 0 | MCG | 64.14 | 3.71 |
bigbench_sports_understanding | 0 | MCG | 55.31 | 1.59 |
bigbench_temporal_sequences | 0 | MCG | 46.92 | 1.4 |
bigbench_tsk_shuff_objs_5 | 0 | MCG | 25.04 | 1.01 |
bigbench_tsk_shuff_objs_7 | 0 | MCG | 15.04 | 0.72 |
bigbench_tsk_shuff_objs_3 | 0 | MCG | 55.33 | 2.75 |
Average: 44.7
TRUTHFULQA
Task | Version | Metric | Value | Std Err |
---|---|---|---|---|
truthfulqa_mc | 1 | mc1 | 30.11 | 1.61 |
truthfulqa_mc | 1 | mc2 | 47.69 | 1.61 |
Average:38.9
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
π Axolotl Configuration
base_model: google/gemma-2b-it
model_type: GemmaForCausalLM
tokenizer_type: GemmaTokenizer
trust_remote_code: true
load_in_8bit: false
load_in_4bit: true
strict: false
rl: dpo
chat_template: chatml
datasets:
- path: mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
split: train
type: chatml.intel
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./out
adapter: qlora
lora_model_dir:
sequence_len: 1800
sample_packing: false
pad_to_sequence_len: false
lora_r: 16
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
wandb_project: axolotl-gemma-dpo
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 5e-7
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false
warmup_steps: 100
evals_per_epoch: 1
eval_table_size:
eval_table_max_new_tokens: 128
save_steps: 1000
max_steps: 1000
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.17.0
- Tokenizers 0.15.0
- axolotl: 0.4.0
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Model tree for Syed-Hasan-8503/openhermes-gemma-2b-it
Base model
google/gemma-2b-it