File size: 3,590 Bytes
9ce7c25 741a805 101e210 9ce7c25 741a805 101e210 741a805 101e210 156d629 101e210 741a805 101e210 cd9fdc6 101e210 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
---
library_name: peft
license: llama2
datasets:
- TuningAI/Startups_V2
language:
- en
pipeline_tag: conversational
tags:
- law
- startups
- finance
- tax
- Algerian
---
## Model Name: **Llama2_13B_startup_Assistant**
## Description:
Llama2_13B_startup_Assistant is a highly specialized language model fine-tuned from Meta's Llama2_13B.
It has been tailored to assist with inquiries related to Algerian startups, offering valuable insights and guidance in these domains.
## Base Model:
This model is based on the Meta's **meta-llama/Llama-2-13b-chat-hf** architecture,
making it a highly capable foundation for generating human-like text responses.
## Dataset :
This model was fine-tuned on a custom dataset meticulously curated with more than 200 unique examples.
The dataset incorporates both manual entries and contributions from GPT3.5, GPT4, and Falcon 180B models.
## Fine-tuning Techniques:
Fine-tuning was performed using QLoRA (Quantized LoRA), an extension of LoRA that introduces quantization for enhanced parameter efficiency.
The model benefits from 4-bit NormalFloat (NF4) quantization and Double Quantization techniques, ensuring optimized performance.
## Performance:
**Llama2_13B_startup_Assistant** exhibits improved performance and efficiency in addressing queries related to Algerian tax law and startups,
making it a valuable resource for individuals and businesses navigating these areas.
## Limitations:
* While highly specialized, this model may not cover every nuanced aspect of Algerian tax law or the startup ecosystem.
* Accuracy may vary depending on the complexity and specificity of questions.
* It may not provide legal advice, and users should seek professional consultation for critical legal matters.
## 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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
```
! huggingface-cli login
```
```python
from transformers import pipeline
from transformers import AutoTokenizer
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM , BitsAndBytesConfig
import torch
#config = PeftConfig.from_pretrained("ayoubkirouane/Llama2_13B_startup_hf")
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=getattr(torch, "float16"),
bnb_4bit_use_double_quant=False)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf",
quantization_config=bnb_config,
device_map={"": 0})
model.config.use_cache = False
model.config.pretraining_tp = 1
model = PeftModel.from_pretrained(model, "TuningAI/Llama2_7B_Cover_letter_generator")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf" , trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
Instruction = "Given a user's information about the target job, you will generate a Cover letter for this job based on this information."
while 1:
input_text = input(">>>")
logging.set_verbosity(logging.CRITICAL)
prompt = f"### Instruction\n{Instruction}.\n ###Input \n\n{input_text}. ### Output:"
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer,max_length=400)
result = pipe(prompt)
print(result[0]['generated_text'].replace(prompt, ''))
``` |