Instructions to use TheBloke/Mistral-7B-Instruct-v0.1-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/Mistral-7B-Instruct-v0.1-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="TheBloke/Mistral-7B-Instruct-v0.1-GPTQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GPTQ")
model = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", device_map="auto")
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/Mistral-7B-Instruct-v0.1-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBloke/Mistral-7B-Instruct-v0.1-GPTQ"
# 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/Mistral-7B-Instruct-v0.1-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ

SGLang

How to use TheBloke/Mistral-7B-Instruct-v0.1-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/Mistral-7B-Instruct-v0.1-GPTQ" \
    --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/Mistral-7B-Instruct-v0.1-GPTQ",
		"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/Mistral-7B-Instruct-v0.1-GPTQ" \
        --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/Mistral-7B-Instruct-v0.1-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TheBloke/Mistral-7B-Instruct-v0.1-GPTQ with Docker Model Runner:
```
docker model run hf.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ
```

AttributeError, When using Specific GPU

by Rohith1016 - opened Nov 16, 2023

Discussion

Rohith1016

Nov 16, 2023

•

edited Nov 16, 2023

Getting This error while using specific GPU
( eg [input_ids = tokenizer(prompt_template, return_tensors='pt').to('cuda:3'])
but when using multiple GPU it is Working Fine [input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()]

ERROR:
Traceback (most recent call last):
File "/home2/asgtestdrive2023/Text2SQL/testvenv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 266, in getattr
return self.data[item]
KeyError: 'shape'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/home2/asgtestdrive2023/Text2SQL/testvenv/lib/python3.10/site-packages/flask/app.py", line 1455, in wsgi_app
response = self.full_dispatch_request()
File "/home2/asgtestdrive2023/Text2SQL/testvenv/lib/python3.10/site-packages/flask/app.py", line 869, in full_dispatch_request
rv = self.handle_user_exception(e)
File "/home2/asgtestdrive2023/Text2SQL/testvenv/lib/python3.10/site-packages/flask/app.py", line 867, in full_dispatch_request
rv = self.dispatch_request()
File "/home2/asgtestdrive2023/Text2SQL/testvenv/lib/python3.10/site-packages/flask/app.py", line 852, in dispatch_request
return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)
File "/home2/asgtestdrive2023/Text2SQL/sql_api.py", line 16, in chat
return jsonify({'response' : get_data(query)})
File "/home2/asgtestdrive2023/Text2SQL/sql_api.py", line 20, in get_data
return text2sql(input)
File "/home2/asgtestdrive2023/Text2SQL/pipeline_v3.py", line 205, in text2sql
output = model.generate(inputs=input_ids, temperature=0.3, do_sample=True, top_p=0.95, top_k=1, max_new_tokens=512)
File "/home2/asgtestdrive2023/Text2SQL/testvenv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home2/asgtestdrive2023/Text2SQL/testvenv/lib/python3.10/site-packages/transformers/generation/utils.py", line 1459, in generate
batch_size = inputs_tensor.shape[0]
File "/home2/asgtestdrive2023/Text2SQL/testvenv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 268, in getattr
raise AttributeError
AttributeError

Rohith1016 changed discussion status to closed Nov 16, 2023

Rohith1016 changed discussion status to open Nov 16, 2023

Rohith1016 changed discussion title from AttributeError to AttributeError, When using Specific GPU Nov 17, 2023

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment