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

Libraries

How to use TheBloke/MixtralRPChat-ZLoss-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="TheBloke/MixtralRPChat-ZLoss-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheBloke/MixtralRPChat-ZLoss-AWQ")
model = AutoModelForCausalLM.from_pretrained("TheBloke/MixtralRPChat-ZLoss-AWQ")
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 TheBloke/MixtralRPChat-ZLoss-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBloke/MixtralRPChat-ZLoss-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheBloke/MixtralRPChat-ZLoss-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TheBloke/MixtralRPChat-ZLoss-AWQ

SGLang

How to use TheBloke/MixtralRPChat-ZLoss-AWQ 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/MixtralRPChat-ZLoss-AWQ" \
    --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": "TheBloke/MixtralRPChat-ZLoss-AWQ",
		"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 "TheBloke/MixtralRPChat-ZLoss-AWQ" \
        --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": "TheBloke/MixtralRPChat-ZLoss-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TheBloke/MixtralRPChat-ZLoss-AWQ with Docker Model Runner:
```
docker model run hf.co/TheBloke/MixtralRPChat-ZLoss-AWQ
```

TheBloke commited on Dec 22, 2023

Commit

bb79e9c

1 Parent(s): ef753f4

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -4

README.md CHANGED Viewed

@@ -14,9 +14,7 @@ license: cc-by-nc-4.0
 model_creator: Charles Goddard
 model_name: MixtralRPChat ZLoss
 model_type: mixtral
-prompt_template: '***System: {system_message} ***Query: {prompt}  ***Response:
-  '
 quantized_by: TheBloke
 tags:
 - mixtral
@@ -66,7 +64,7 @@ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization metho
 AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
-AWQ models are supported by (note that note all of these may support Mixtral models yet):
 - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
 - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.

 model_creator: Charles Goddard
 model_name: MixtralRPChat ZLoss
 model_type: mixtral
+prompt_template: '***System: {system_message} ***Query: {prompt}  ***Response:'
 quantized_by: TheBloke
 tags:
 - mixtral
 AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
+AWQ models are supported by (note that not all of these may support Mixtral models yet):
 - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
 - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.