Instructions to use kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2 with Transformers:

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

pipe = pipeline("text-generation", model="kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2")
model = AutoModelForCausalLM.from_pretrained("kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2

SGLang

How to use kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2 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 "kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2" \
    --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": "kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2",
		"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 "kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2" \
        --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": "kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2 with Docker Model Runner:
```
docker model run hf.co/kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2
```

kyujinpy commited on Dec 26, 2023

Commit

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1 Parent(s): aa9ad8b

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 ---
 license: cc-by-nc-sa-4.0
 ---

 ---
+language:
+- en
+datasets:
+- kyujinpy/orca_math_dpo
+pipeline_tag: text-generation
 license: cc-by-nc-sa-4.0
 ---
+# **Sakura-SOLRCA-Math-Instruct-DPO-v2**
+<img src='./sakura.png' width=512>
+## Model Details
+**Model Developers** Kyujin Han (kyujinpy)
+**Method**
+Using DPO method.
+With [Intel/orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) and [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo).
+I shared the merge version [kyujinpy/orca_math_dpo](https://huggingface.co/datasets/kyujinpy/orca_math_dpo).
+I will share the information about my model. (training and code)
+Please see: ⭐[Sakura-SOLAR(will update)]().
+# **Model Benchmark**
+## Open leaderboard
+- Follow up as [link](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
+| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| Sakura-SOLRCA-Math-Instruct-DPO-v2 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
+| Sakura-SOLRCA-Math-Instruct-DPO-v1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
+| Sakura-SOLRCA-Instruct-DPO | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
+| Sakura-SOLAR-Instruct-DPO-v2 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
+| Sakura-SOLAR-Instruct-DPO-v1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
+| [kyujinpy/Sakura-SOLAR-Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct) | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
+# Implementation Code
+```python
+### KO-Platypus
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+repo = "kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2"
+OpenOrca = AutoModelForCausalLM.from_pretrained(
+        repo,
+        return_dict=True,
+        torch_dtype=torch.float16,
+        device_map='auto'
+)
+OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
+```
+---

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