Instructions to use dddsaty/Merge_Sakura_Solar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dddsaty/Merge_Sakura_Solar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dddsaty/Merge_Sakura_Solar", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dddsaty/Merge_Sakura_Solar") model = AutoModelForCausalLM.from_pretrained("dddsaty/Merge_Sakura_Solar", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dddsaty/Merge_Sakura_Solar with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dddsaty/Merge_Sakura_Solar" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dddsaty/Merge_Sakura_Solar", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/dddsaty/Merge_Sakura_Solar
- SGLang
How to use dddsaty/Merge_Sakura_Solar 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 "dddsaty/Merge_Sakura_Solar" \ --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": "dddsaty/Merge_Sakura_Solar", "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 "dddsaty/Merge_Sakura_Solar" \ --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": "dddsaty/Merge_Sakura_Solar", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use dddsaty/Merge_Sakura_Solar with Docker Model Runner:
docker model run hf.co/dddsaty/Merge_Sakura_Solar
Update README.md
Browse files
README.md
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- [Sakura-SOLRCA-Math-Instruct-DPO-v2](https://huggingface.co/kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2)
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- [Sakura-SOLRCA-Instruct-DPO](https://huggingface.co/kyujinpy/Sakura-SOLRCA-Instruct-DPO)
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**
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parameters:
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density: 1.0
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weight: 1.0
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- model: kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2
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parameters:
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density: 0.5
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weight: [0.33, 0.4, 0.33]
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- model: kyujinpy/Sakura-SOLRCA-Instruct-DPO
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parameters:
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density: [0.33, 0.45, 0.66]
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weight: 0.66
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merge_method: dare_ties
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base_model: kyujinpy/Sakura-SOLAR-Instruct
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parameters:
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normalize: true
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int8_mask: true
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dtype: bfloat16
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tokenizer_source : union
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```
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**Original Author's HuggingFace profile**
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- [kyujinpy](https://huggingface.co/kyujinpy)
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- [Sakura-SOLRCA-Math-Instruct-DPO-v2](https://huggingface.co/kyujinpy/Sakura-SOLRCA-Math-Instruct-DPO-v2)
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- [Sakura-SOLRCA-Instruct-DPO](https://huggingface.co/kyujinpy/Sakura-SOLRCA-Instruct-DPO)
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**Score**
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|Average|ARC|HellaSwag|MMLU|TruthfulQA|Winogrande|GSM8K|
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|74.03|70.73|88.51|66.03|72.21|82.72|63.99|
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**Original Author's HuggingFace profile**
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- [kyujinpy](https://huggingface.co/kyujinpy)
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