Instructions to use IgnoraZ/llama3_synthquestions_dpo_100k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IgnoraZ/llama3_synthquestions_dpo_100k with Transformers:

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

pipe = pipeline("text-generation", model="IgnoraZ/llama3_synthquestions_dpo_100k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IgnoraZ/llama3_synthquestions_dpo_100k")
model = AutoModelForCausalLM.from_pretrained("IgnoraZ/llama3_synthquestions_dpo_100k")
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

vLLM

How to use IgnoraZ/llama3_synthquestions_dpo_100k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IgnoraZ/llama3_synthquestions_dpo_100k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IgnoraZ/llama3_synthquestions_dpo_100k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IgnoraZ/llama3_synthquestions_dpo_100k

SGLang

How to use IgnoraZ/llama3_synthquestions_dpo_100k 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 "IgnoraZ/llama3_synthquestions_dpo_100k" \
    --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": "IgnoraZ/llama3_synthquestions_dpo_100k",
		"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 "IgnoraZ/llama3_synthquestions_dpo_100k" \
        --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": "IgnoraZ/llama3_synthquestions_dpo_100k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IgnoraZ/llama3_synthquestions_dpo_100k with Docker Model Runner:
```
docker model run hf.co/IgnoraZ/llama3_synthquestions_dpo_100k
```

nielsr HF Staff commited on Jun 18, 2025

Commit

2d1ae1d

verified ·

1 Parent(s): 823616c

Add library name and pipeline tag

Browse files

This PR adds the appropriate library name and pipeline tag to ensure wider visibility in the model hub!

Files changed (1) hide show

README.md +5 -3

README.md CHANGED Viewed

@@ -1,9 +1,11 @@
 ---
-license: cc-by-4.0
-language:
-- en
 base_model:
 - IgnoraZ/llama3_synthquestions_1m
 ---
 # Model Card for Model ID

 ---
 base_model:
 - IgnoraZ/llama3_synthquestions_1m
+language:
+- en
+license: cc-by-4.0
+library_name: transformers
+pipeline_tag: text-generation
 ---
 # Model Card for Model ID