Instructions to use TheBloke/openchat_v2_openorca_preview-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/openchat_v2_openorca_preview-GPTQ with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TheBloke/openchat_v2_openorca_preview-GPTQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheBloke/openchat_v2_openorca_preview-GPTQ")
model = AutoModelForCausalLM.from_pretrained("TheBloke/openchat_v2_openorca_preview-GPTQ")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TheBloke/openchat_v2_openorca_preview-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBloke/openchat_v2_openorca_preview-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheBloke/openchat_v2_openorca_preview-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TheBloke/openchat_v2_openorca_preview-GPTQ

SGLang

How to use TheBloke/openchat_v2_openorca_preview-GPTQ 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 "TheBloke/openchat_v2_openorca_preview-GPTQ" \
    --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": "TheBloke/openchat_v2_openorca_preview-GPTQ",
		"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 "TheBloke/openchat_v2_openorca_preview-GPTQ" \
        --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": "TheBloke/openchat_v2_openorca_preview-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheBloke/openchat_v2_openorca_preview-GPTQ with Docker Model Runner:
```
docker model run hf.co/TheBloke/openchat_v2_openorca_preview-GPTQ
```

cherry0328 commited on Jan 6

Commit

0d0eeb8

verified ·

1 Parent(s): 5a4c2ea

Update README.md

Browse files

Your model is truly impressive! We would love to contribute by updating the README to include the base_model information. This will help address the missing details in the model card.

Files changed (1) hide show

README.md +3 -1

README.md CHANGED Viewed

@@ -4,6 +4,8 @@ datasets:
 inference: false
 license: other
 model_type: llama
 ---
 <!-- header start -->
@@ -289,4 +291,4 @@ def tokenize_single_input(tokenizer, prompt):
 To explore conditional language models, you can also set `prefix = "Assistant GPT3:"` to mimic ChatGPT behavior (this may cause performance degradation).
-*Hint: In BPE, `tokenize(A) + tokenize(B)` does not always equals to `tokenize(A + B)`*

 inference: false
 license: other
 model_type: llama
+base_model:
+- openchat/openchat_v2_openorca_preview
 ---
 <!-- header start -->
 To explore conditional language models, you can also set `prefix = "Assistant GPT3:"` to mimic ChatGPT behavior (this may cause performance degradation).
+*Hint: In BPE, `tokenize(A) + tokenize(B)` does not always equals to `tokenize(A + B)`*