Instructions to use Codex07/Lora_1B_TR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Codex07/Lora_1B_TR with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/home/tk/Desktop/Folder/projects/AI/Models/Llama-3.2-1B-Instruct/")
model = PeftModel.from_pretrained(base_model, "Codex07/Lora_1B_TR")

Transformers

How to use Codex07/Lora_1B_TR with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Codex07/Lora_1B_TR")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Codex07/Lora_1B_TR", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Codex07/Lora_1B_TR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Codex07/Lora_1B_TR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Codex07/Lora_1B_TR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Codex07/Lora_1B_TR

SGLang

How to use Codex07/Lora_1B_TR with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Codex07/Lora_1B_TR" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Codex07/Lora_1B_TR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Codex07/Lora_1B_TR" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Codex07/Lora_1B_TR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Codex07/Lora_1B_TR with Docker Model Runner:
```
docker model run hf.co/Codex07/Lora_1B_TR
```

Lora_1B_TR / README.md

Codex07

Update README.md

6bd6734 verified 10 months ago

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metadata

library_name: peft
model_name: lora_1B_TR
tags:
  - meta-llama/Llama-3.2-1B-Instruct
  - lora
  - sft
  - transformers
  - trl
  - unsloth
licence: license
pipeline_tag: text-generation
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets:
  - kadirnar/combined-turkish-datasets-v5
language:
  - tr
  - en

Model Card for Lora_TR_1B

This is a Lora Adaptor of 'meta-llama/Llama-3.2-1B-Instruct'. The main goal of this adapter is to obtain an Llama who speaks Turkish better.

(r=32, lora_alpha=64, lora_dropout=0.005)

Quick start

from unsloth import FastLanguageModel
from peft import PeftModel
from transformers import AutoTokenizer

BASE = "meta-llama/Llama-3.2-1B-Instruct"
ADAPTER = "Codex07/Lora_1B_TR"

# Load Model
model, tok = FastLanguageModel.from_pretrained(
    model_name=BASE, max_seq_length=2048, load_in_4bit=False, dtype=None, device_map="auto"
)
# Load Adaptor
model = PeftModel.from_pretrained(model, ADAPTER)  # adapter’ı Unsloth modeline tak
FastLanguageModel.for_inference(model)

# Test
messages = [
    {"role":"system","content":"You are AI assistant. Give user answers"},# Sen bir Yapay Zeka Asistanısısın. kullanıcıdan gelen sorulara resmi cevap ver.
    {"role":"user","content":"Selam!"}
]
prompt = tok.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(prompt, max_new_tokens=2048)
print(tok.decode(out[0, prompt.shape[-1]:], skip_special_tokens=True))

Training procedure

Half of 'kadirnar/combined-turkish-datasets-v5' Turkish dataset used. Dataset divided into chunks by size 65k.

1> 2:50:33 / 2.746500 -> 1.771400 / 5.1.0
2> 3:00:00 / 1.7 -> 1.7           / 5.1.1
3> 2:18:19 / 1.859100 -> 1.474300 / 5.1.2
4> 3:15:13 / 1.421800 -> 1.122000 / 5.1.3
5> 2:50:00 / 1.746600 -> 1.629600 / 5.1.0
6> 2:44:46 / 1.745000 -> 1.653300 / 5.1.1
7> 2:07:00 / 1.478200 -> 1.357400 / 5.1.2
8> 3:11:54 / 1.174700 -> 1.046100 / 5.1.3
9> 3:12:39 / 1.117600 -> 0.796700 / 5.2.0
10>1:00:57 / 2.217400 -> 1.741400 / 5.2.1
11>1:30:04 / 2.919900 -> 2.534300 / 5.2.2
12>1:30:05 / 2.534300 -> 2.320100 / 5.2.2

This model was trained with SFT.

Framework versions

PEFT 0.17.1
TRL: 0.23.0
Transformers: 4.56.2
Pytorch: 2.8.0
Datasets: 4.3.0
Tokenizers: 0.22.1

Citations

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}