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

Libraries

How to use TheBloke/Mistral-7B-OpenOrca-GPTQ with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheBloke/Mistral-7B-OpenOrca-GPTQ")
model = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-OpenOrca-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-OpenOrca-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-OpenOrca-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-OpenOrca-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TheBloke/Mistral-7B-OpenOrca-GPTQ

SGLang

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

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

IndexError: Out of range: piece id is out of range

by latent-variable - opened Oct 4, 2023

Discussion

latent-variable

Oct 4, 2023

Using Exllama & Exllamav2 I get the following error with this model in the Text-generation-webui

Traceback (most recent call last):
File "C:\AI\text-generation-webui\modules\text_generation.py", line 384, in generate_reply_custom
for reply in shared.model.generate_with_streaming(question, state):
File "C:\AI\text-generation-webui\modules\exllamav2.py", line 119, in generate_with_streaming
decoded_text = self.tokenizer.decode(ids[:, initial_len:])[0]
File "C:\AI\text-generation-webui\installer_files\env\lib\site-packages\exllamav2\tokenizer.py", line 92, in decode
texts.append(self.tokenizer.Decode(seq))
File "C:\AI\text-generation-webui\installer_files\env\lib\site-packages\sentencepiece_init_.py", line 780, in Decode
return self.DecodeIds(input)
File "C:\AI\text-generation-webui\installer_files\env\lib\site-packages\sentencepiece_init.py", line 337, in _DecodeIds
return _sentencepiece.SentencePieceProcessor__DecodeIds(self, ids)
IndexError: Out of range: piece id is out of range.
Output generated in 4.94 seconds (53.20 tokens/s, 263 tokens, context 1071, seed 587620518)

latent-variable changed discussion status to closed Oct 4, 2023

latent-variable changed discussion status to open Oct 4, 2023

ryanstout

Oct 4, 2023

Same issue here, the error is coming from the sentencepiece library, perhaps something with the vocabulary size isn't set correctly.

Imathenerd

Oct 5, 2023

Is that related to this at all? Traceback (most recent call last):
File "c:\Users\Scott\VSC_Source\LLM_DB\EXLLAMAv2y production.py", line 181, in
output = generator.generate_simple(prompt,settings,max_new_tokens)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Scott\anaconda3\envs\ELLAMA\Lib\site-packages\exllamav2\generator\base.py", line 60, in generate_simple
token, _ = ExLlamaV2Sampler.sample(logits, gen_settings, self.sequence_ids, random.random())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Scott\anaconda3\envs\ELLAMA\Lib\site-packages\exllamav2\generator\sampler.py", line 63, in sample
if settings.token_bias is not None: logits += settings.token_bias
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: The size of tensor a (32032) must match the size of tensor b (32002) at non-singleton dimension 1

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