Instructions to use cgus/MiniChat-3B-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cgus/MiniChat-3B-exl2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cgus/MiniChat-3B-exl2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cgus/MiniChat-3B-exl2") model = AutoModelForCausalLM.from_pretrained("cgus/MiniChat-3B-exl2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use cgus/MiniChat-3B-exl2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cgus/MiniChat-3B-exl2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cgus/MiniChat-3B-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cgus/MiniChat-3B-exl2
- SGLang
How to use cgus/MiniChat-3B-exl2 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 "cgus/MiniChat-3B-exl2" \ --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": "cgus/MiniChat-3B-exl2", "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 "cgus/MiniChat-3B-exl2" \ --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": "cgus/MiniChat-3B-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cgus/MiniChat-3B-exl2 with Docker Model Runner:
docker model run hf.co/cgus/MiniChat-3B-exl2
Update README.md
Browse files
README.md
CHANGED
|
@@ -23,6 +23,17 @@ Model creator: [GeneZC](https://huggingface.co/GeneZC)
|
|
| 23 |
|
| 24 |
I originally planned to make both h6 and h8 versions for each quant but there was consistent 30MB difference between h6 and h8.
|
| 25 |
So I don't see much of a reason to upload the rest of h6.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 26 |
|
| 27 |
# Original model card:
|
| 28 |
|
|
|
|
| 23 |
|
| 24 |
I originally planned to make both h6 and h8 versions for each quant but there was consistent 30MB difference between h6 and h8.
|
| 25 |
So I don't see much of a reason to upload the rest of h6.
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
## How to run
|
| 29 |
+
|
| 30 |
+
This quantization method uses GPU and requires Exllamav2 loader which can be found in following applications:
|
| 31 |
+
|
| 32 |
+
[Text Generation Webui](https://github.com/oobabooga/text-generation-webui)
|
| 33 |
+
|
| 34 |
+
[KoboldAI](https://github.com/henk717/KoboldAI)
|
| 35 |
+
|
| 36 |
+
[ExUI](https://github.com/turboderp/exui)
|
| 37 |
|
| 38 |
# Original model card:
|
| 39 |
|