Instructions to use openchat/openchat_3.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openchat/openchat_3.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="openchat/openchat_3.5")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openchat/openchat_3.5", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use openchat/openchat_3.5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openchat/openchat_3.5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openchat/openchat_3.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/openchat/openchat_3.5
- SGLang
How to use openchat/openchat_3.5 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 "openchat/openchat_3.5" \ --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": "openchat/openchat_3.5", "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 "openchat/openchat_3.5" \ --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": "openchat/openchat_3.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use openchat/openchat_3.5 with Docker Model Runner:
docker model run hf.co/openchat/openchat_3.5
Update README.md
Browse files
README.md
CHANGED
|
@@ -12,6 +12,7 @@ datasets:
|
|
| 12 |
- LDJnr/Verified-Camel
|
| 13 |
- tiedong/goat
|
| 14 |
- glaiveai/glaive-code-assistant
|
|
|
|
| 15 |
library_name: transformers
|
| 16 |
pipeline_tag: text-generation
|
| 17 |
---
|
|
@@ -170,6 +171,7 @@ OpenChat 3.5 was trained with C-RLFT on a collection of publicly available high-
|
|
| 170 |
- Capybara [1](https://huggingface.co/datasets/LDJnr/Pure-Dove) [2](https://huggingface.co/datasets/LDJnr/Verified-Camel) [3](https://huggingface.co/datasets/LDJnr/LessWrong-Amplify-Instruct)
|
| 171 |
- [GOAT](https://huggingface.co/datasets/tiedong/goat)
|
| 172 |
- [Glaive](https://huggingface.co/datasets/glaiveai/glaive-code-assistant)
|
|
|
|
| 173 |
|
| 174 |
## Citation
|
| 175 |
|
|
|
|
| 12 |
- LDJnr/Verified-Camel
|
| 13 |
- tiedong/goat
|
| 14 |
- glaiveai/glaive-code-assistant
|
| 15 |
+
- meta-math/MetaMathQA
|
| 16 |
library_name: transformers
|
| 17 |
pipeline_tag: text-generation
|
| 18 |
---
|
|
|
|
| 171 |
- Capybara [1](https://huggingface.co/datasets/LDJnr/Pure-Dove) [2](https://huggingface.co/datasets/LDJnr/Verified-Camel) [3](https://huggingface.co/datasets/LDJnr/LessWrong-Amplify-Instruct)
|
| 172 |
- [GOAT](https://huggingface.co/datasets/tiedong/goat)
|
| 173 |
- [Glaive](https://huggingface.co/datasets/glaiveai/glaive-code-assistant)
|
| 174 |
+
- [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA)
|
| 175 |
|
| 176 |
## Citation
|
| 177 |
|