metadata
license: gemma
base_model: google/gemma-7b
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- masakhane/african-ultrachat
- israel/untrachat_en
model-index:
- name: zephyr-7b-gemma-sft-african-ultrachat-5k
results: []
zephyr-7b-gemma-sft-african-ultrachat-5k
This model is a fine-tuned version of google/gemma-7b on the masakhane/african-ultrachat and the israel/untrachat_en datasets. It achieves the following results on the evaluation set:
- Loss: 1.1356
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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.1994 | 1.0 | 2480 | 1.1954 |
| 1.0039 | 2.0 | 4960 | 1.0974 |
| 0.6836 | 3.0 | 7440 | 1.1356 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
Usage
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="
zephyr-7b-gemma-sft-african-ultrachat-5k", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who answewrs question in given language",
},
{"role": "user", "content": "what is the 3 biggest countrys in Africa?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate<eos>
# <|user|>
# what is the 3 biggest countrys in Africa?<eos>
# <|assistant|>
# The 3 biggest countries in Africa are Nigeria, Ethiopia and South Africa.
Quantized Versions through bitsandbytes
import torch
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("
zephyr-7b-gemma-sft-african-ultrachat-5k")
model = AutoModelForCausalLM.from_pretrained("
zephyr-7b-gemma-sft-african-ultrachat-5k", quantization_config=quantization_config)
pipe = pipeline("text-generation", model=model,tokenizer=tokenizer, torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who answewrs question in given language",
},
{"role": "user", "content": "list languages in Africa?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])