Text Generation
Transformers
Safetensors
English
llama
text-generation-inference
unsloth
trl
conversational
Instructions to use beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged") model = AutoModelForCausalLM.from_pretrained("beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged") 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 beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged
- SGLang
How to use beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged 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 "beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged" \ --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": "beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged", "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 "beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged" \ --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": "beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged", max_seq_length=2048, ) - Docker Model Runner
How to use beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged with Docker Model Runner:
docker model run hf.co/beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged
KoAlpaca-RealQA-Solar-Ko-Recovery-11B-Merged
LoRA merged model from beomi/KoAlpaca-RealQA-Solar-Ko-Recovery-11B
- Developed by: beomi
- License: apache-2.0
- Finetuned from model : beomi/Solar-Ko-Recovery-11B
Eval with LogicKor
Default
| Category | Single turn | Multi turn |
|---|---|---|
| ์ดํด(Understanding) | 8.43 | 5.86 |
| ์ํ(Math) | 3.86 | 1.00 |
| ๊ธ์ฐ๊ธฐ(Writing) | 9.71 | 7.71 |
| ์ถ๋ก (Reasoning) | 7.57 | 3.86 |
| ์ฝ๋ฉ(Coding) | 7.57 | 7.00 |
| ๋ฌธ๋ฒ(Grammar) | 7.43 | 4.14 |
| Category | Score |
|---|---|
| Single turn | 7.43 |
| Multi turn | 4.93 |
| Overall | 6.18 |
1-Shot
| Category | Single turn | Multi turn |
|---|---|---|
| ์ถ๋ก (Reasoning) | 7.57 | 4.43 |
| ์ฝ๋ฉ(Coding) | 7.29 | 8.57 |
| ์ํ(Math) | 2.86 | 1.71 |
| ๊ธ์ฐ๊ธฐ(Writing) | 9.29 | 7.57 |
| ์ดํด(Understanding) | 7.43 | 5.86 |
| ๋ฌธ๋ฒ(Grammar) | 7.29 | 4.71 |
| Category | Score |
|---|---|
| Single turn | 6.95 |
| Multi turn | 5.48 |
| Overall | 6.21 |
CoT-1-Shot
| Category | Single turn | Multi turn |
|---|---|---|
| ์ฝ๋ฉ(Coding) | 7.14 | 8.14 |
| ์ถ๋ก (Reasoning) | 6.86 | 3.57 |
| ๊ธ์ฐ๊ธฐ(Writing) | 9.71 | 7.71 |
| ์ํ(Math) | 2.29 | 1.14 |
| ์ดํด(Understanding) | 7.43 | 7.00 |
| ๋ฌธ๋ฒ(Grammar) | 7.29 | 3.14 |
| Category | Score |
|---|---|
| Single turn | 6.79 |
| Multi turn | 5.12 |
| Overall | 5.95 |
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