Instructions to use MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3 with Transformers:

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

pipe = pipeline("text-generation", model="MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3")
model = AutoModelForMultimodalLM.from_pretrained("MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3

SGLang

How to use MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3 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 "MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3" \
    --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": "MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3",
		"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 "MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3" \
        --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": "MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3 with Docker Model Runner:
```
docker model run hf.co/MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3
```

Qwen2.5-Coder-1.5B LoRA Fine-tuned (DEEP Dataset)

Bu model, Qwen2.5-Coder-1.5B-Instruct base modeli kullanılarak DEEP dataset üzerinde LoRA ile fine-tune edilmiş ve base model ile merge edilmiştir.

🎯 Model Açıklaması

Base Model: Qwen/Qwen2.5-Coder-1.5B-Instruct
Dataset: Naholav/CodeGen-DEEP-5K
Training Step: 1128
Method: LoRA (Low-Rank Adaptation)
Merge Status: Base model ile merge edildi

📊 Training Hyperparameters

Learning Rate: 1.5e-4
LoRA Rank: 32
LoRA Alpha: 64
LoRA Dropout: 0.08
Target Modules: q_proj, k_proj, v_proj, o_proj
Batch Size: 8
Epochs: 4
Context Length: 1024
Optimizer: paged_adamw_8bit
Scheduler: Cosine
Weight Decay: 0.01
Warmup Ratio: 0.05

Eğitim Sürecinin Grafikleri

Kullanım

Basit Kullanım

from transformers import AutoModelForCausalLM, AutoTokenizer

# Model ve tokenizer'ı yükle
model = AutoModelForCausalLM.from_pretrained(
    "MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3")

# Kod üret
prompt = "Write a Python function to calculate the factorial of a number"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_length=512,
    temperature=0.7,
    top_p=0.95,
    do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

System Prompt ile Kullanım

messages = [
    {"role": "system", "content": "You are an expert Python programmer. Please read the problem carefully before writing any Python code."},
    {"role": "user", "content": "Write a function to check if a string is a palindrome"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📈 Evaluation Results

Validation Loss: 0.963
Test Loss: 0.XXX
Pass@1: XX%

💾 Model Size

Parameters: ~1.5B
Size: ~3GB (FP16)

⚠️ Limitations

Model, 1024 token context length ile eğitilmiştir
Sadece Python kod üretimi için optimize edilmiştir
Reasoning trace'leri içermez (sadece solution field kullanıldı)

Downloads last month: -

Safetensors

Model size

2B params

Tensor type

F32

Model tree for MehmetDORA/qwen2.5-coder-1.5b-deep-lora-merged-deneme3

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B-Instruct

Adapter

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this model

MehmetDORA
/

qwen2.5-coder-1.5b-deep-lora-merged-deneme3