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---
language:
- tr
library_name: peft
datasets:
- atasoglu/databricks-dolly-15k-tr
pipeline_tag: text-generation
base_model: meta-llama/Llama-2-7b-hf
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
# How to use:
```
!pip install transformers peft accelerate bitsandbytes trl safetensors
from huggingface_hub import notebook_login
notebook_login()
import torch
from peft import AutoPeftModelForCausalLM, get_peft_config, PeftModel, PeftConfig, get_peft_model, LoraConfig, TaskType
from transformers import AutoTokenizer
peft_model_id = "akdeniz27/llama-2-7b-hf-qlora-dolly15k-turkish"
config = PeftConfig.from_pretrained(peft_model_id)
# load base LLM model and tokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
peft_model_id,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
load_in_4bit=True,
)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
prompt = "..."
input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()
outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.9)
``` |