Quantization made by Richard Erkhov.
VeriUS-LLM-8b-v0.2 - AWQ
Original model description:
license: llama3
base_model: unsloth/llama-3-8b-bnb-4bit
VeriUS LLM 8b v0.2
VeriUS LLM is a instruct following large language model supporting Turkish language based on llama3-8B.
Model Details
Base Model: unsloth/llama-3-8b-bnb-4bit
Training Dataset: A carefully curated general domain Turkish instruction dataset.
Training Method: Fine-tuned using QLoRA and ORPO
#TrainingArguments
PER_DEVICE_BATCH_SIZE: 2
GRADIENT_ACCUMULATION_STEPS: 4
WARMUP_RATIO: 0.03
NUM_EPOCHS: 2
LR: 0.000008
OPTIM: "adamw_8bit"
WEIGHT_DECAY: 0.01
LR_SCHEDULER_TYPE: "linear"
BETA: 0.1
#PEFT Arguments
RANK: 128
TARGET_MODULES:
- "q_proj"
- "k_proj"
- "v_proj"
- "o_proj"
- "gate_proj"
- "up_proj"
- "down_proj"
LORA_ALPHA: 256
LORA_DROPOUT: 0
BIAS: "none"
GRADIENT_CHECKPOINT: 'unsloth'
USE_RSLORA: false\
Usage
This model is trained used Unsloth and uses it for fast inference. For Unsloth installation please refer to:
https://github.com/unslothai/unslothThis model can also be loaded with AutoModelForCausalLM
How to load with unsloth:
1from unsloth import FastLanguageModel
2
3max_seq_len = 1024
4model, tokenizer = FastLanguageModel.from_pretrained(
5 model_name="VeriUs/VeriUS-LLM-8b-v0.2",
6 max_seq_length=max_seq_len,
7 dtype=None
8)
9FastLanguageModel.for_inference(model) # Enable native 2x faster inference
10
11prompt_tempate = """Aşağıda, görevini açıklayan bir talimat ve daha fazla bağlam sağlayan bir girdi verilmiştir. İsteği uygun bir şekilde tamamlayan bir yanıt yaz.
12
13### Talimat:
14{}
15
16### Girdi:
17{}
18
19### Yanıt:
20"""
21
22
23def generate_output(instruction, user_input):
24 input_ids = tokenizer(
25 [
26 prompt_tempate.format(instruction, user_input)
27 ], return_tensors="pt").to("cuda")
28
29 outputs = model.generate(**input_ids, max_length=max_seq_len, do_sample=True)
30
31 # removes prompt, comment this out if you want to see it.
32 outputs = [output[len(input_ids[i].ids):] for i, output in enumerate(outputs)]
33
34 return tokenizer.decode(outputs[0], skip_special_tokens=True)
35
36
37response = generate_output("Türkiye'nin en kalabalık şehri hangisidir?", "")
38print(response)
Bias, Risks, and Limitations
Limitations and Known Biases
Primary Function and Application: VeriUS LLM, an autoregressive language model, is primarily designed to predict the next token in a text string. While often used for various applications, it is important to note that it has not undergone extensive real-world application testing. Its effectiveness and reliability across diverse scenarios remain largely unverified.
Language Comprehension and Generation: The base model is primarily trained in standard English. Even though it fine-tuned with and Turkish dataset, its performance in understanding and generating slang, informal language, or other languages may be limited, leading to potential errors or misinterpretations.
Generation of False Information: Users should be aware that VeriUS LLM may produce inaccurate or misleading information. Outputs should be considered as starting points or suggestions rather than definitive answers.
