Instructions to use ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B with Transformers:

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

pipe = pipeline("text-generation", model="ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B")
model = AutoModelForCausalLM.from_pretrained("ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B

SGLang

How to use ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B 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 "ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B" \
    --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": "ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B",
		"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 "ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B" \
        --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": "ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B with Docker Model Runner:
```
docker model run hf.co/ranjanrajib/DPO-trained-Qwen2.5-Python-Coder-7B
```

ranjanrajib commited on Sep 15

Commit

bedfbad

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1 Parent(s): bb51753

Update README.md

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README.md +13 -18

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@@ -1,25 +1,20 @@
 ---
 license: apache-2.0
----
-language: en
-license: apache-2.0
-library_name: transformers
 pipeline_tag: text-generation
-base_model: Qwen/Qwen2.5-Coder-7B-Instruct
 tags:
-* qwen2.5
-* qwen
-* code-generation
-* python
-* dpo
-* direct-preference-optimization
-* preference-learning
-* llm-alignment
-* lora
----
 # Qwen2.5-Coder-7B-Instruct — DPO Fine-Tuned Model
@@ -369,4 +364,4 @@ The resulting model demonstrates improved held-out coding performance relative t
 The experiment therefore provides both a practical DPO fine-tuning pipeline and a case study in the distinction between **optimizing a preference objective and improving downstream model capability**.
-**This is an ongoing project. Will be finetuned based on further results.**

 ---
 license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Coder-7B-Instruct
 pipeline_tag: text-generation
 tags:
+- qwen2.5
+- qwen
+- python
+- dpo
+- direct-preference-generation
+- preference-learning
+- llm-alignment
+- lora
 # Qwen2.5-Coder-7B-Instruct — DPO Fine-Tuned Model
 The experiment therefore provides both a practical DPO fine-tuning pipeline and a case study in the distinction between **optimizing a preference objective and improving downstream model capability**.
+**This is an ongoing project. Will be finetuned based on further results.**