Text Generation
Transformers
Safetensors
English
Korean
multilingual
darwin
darwin-family
darwin-duo
duo
ensemble
mixture-of-models
router
korean
reasoning
finalbench
vidraft
Eval Results (legacy)
Eval Results
Instructions to use FINAL-Bench/Darwin-60B-DUO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINAL-Bench/Darwin-60B-DUO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FINAL-Bench/Darwin-60B-DUO")# Load model directly from transformers import DarwinDuoOrchestrator model = DarwinDuoOrchestrator.from_pretrained("FINAL-Bench/Darwin-60B-DUO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use FINAL-Bench/Darwin-60B-DUO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FINAL-Bench/Darwin-60B-DUO" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FINAL-Bench/Darwin-60B-DUO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/FINAL-Bench/Darwin-60B-DUO
- SGLang
How to use FINAL-Bench/Darwin-60B-DUO 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 "FINAL-Bench/Darwin-60B-DUO" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FINAL-Bench/Darwin-60B-DUO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "FINAL-Bench/Darwin-60B-DUO" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FINAL-Bench/Darwin-60B-DUO", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use FINAL-Bench/Darwin-60B-DUO with Docker Model Runner:
docker model run hf.co/FINAL-Bench/Darwin-60B-DUO
Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
|
@@ -35,10 +35,12 @@ model-index:
|
|
| 35 |
name: Accuracy
|
| 36 |
---
|
| 37 |
|
| 38 |
-
> ### 📱
|
| 39 |
> VIDRAFT's on-device family: a 35B model that runs on **iPhone** and on **CPU with no GPU** — stock `llama.cpp`, no fork.
|
| 40 |
>
|
| 41 |
-
> [**
|
| 39 |
> VIDRAFT's on-device family: a 35B model that runs on **iPhone** and on **CPU with no GPU** — stock `llama.cpp`, no fork.
|
| 40 |
>
|
| 41 |
+
> [](https://huggingface.co/spaces/FINAL-Bench/POCKET-35B-CPU) [](https://huggingface.co/collections/FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6) [](https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF) [](https://huggingface.co/FINAL-Bench/POCKET-KR-MLX) [](https://huggingface.co/FINAL-Bench/POCKET-EN-GGUF)
|
| 42 |
+
|
| 43 |
+
>
|
| 44 |
|
| 45 |
|
| 46 |
<p align="center">
|