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Instructions to use TheBloke/Mixtral-8x7B-MoE-RP-Story-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

Transformers

How to use TheBloke/Mixtral-8x7B-MoE-RP-Story-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="TheBloke/Mixtral-8x7B-MoE-RP-Story-GPTQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("TheBloke/Mixtral-8x7B-MoE-RP-Story-GPTQ")
model = AutoModelForMultimodalLM.from_pretrained("TheBloke/Mixtral-8x7B-MoE-RP-Story-GPTQ")
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/Mixtral-8x7B-MoE-RP-Story-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBloke/Mixtral-8x7B-MoE-RP-Story-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/Mixtral-8x7B-MoE-RP-Story-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TheBloke/Mixtral-8x7B-MoE-RP-Story-GPTQ

SGLang

How to use TheBloke/Mixtral-8x7B-MoE-RP-Story-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/Mixtral-8x7B-MoE-RP-Story-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/Mixtral-8x7B-MoE-RP-Story-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/Mixtral-8x7B-MoE-RP-Story-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/Mixtral-8x7B-MoE-RP-Story-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TheBloke/Mixtral-8x7B-MoE-RP-Story-GPTQ with Docker Model Runner:
```
docker model run hf.co/TheBloke/Mixtral-8x7B-MoE-RP-Story-GPTQ
```

ValueError: ## Could not find model.layers.0.mlp.down_proj.* in model

by deleted - opened Dec 15, 2023

Discussion

deleted

Dec 15, 2023

Getting an error while loading

Raaniel

Dec 15, 2023

Hi, I am experiencing similiar error while loading a model. ValueError: Unrecognized layer: model.layers.0.block_sparse_moe.experts.0.w1.bias

LLLLMAfficionado

Dec 16, 2023

Same here, tried 32, 128 etc, same error.

Renegadesoffun

Dec 16, 2023

hmm i played with loading it with autogptq and it said it loaded on my 24gb vram but for first response got this error
RuntimeError: cannot reshape tensor of 0 elements into shape [-1, 1, 0] because the unspecified dimension size -1 can be any value and is ambiguous

dunno what im doin wrong

Traceback (most recent call last):
File "D:\booga\text-generation-webui\modules\callbacks.py", line 57, in gentask
ret = self.mfunc(callback=_callback, *args, **self.kwargs)
File "D:\booga\text-generation-webui\modules\text_generation.py", line 351, in generate_with_callback
shared.model.generate(**kwargs)
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\transformers\generation\utils.py", line 1764, in generate
return self.sample(
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\transformers\generation\utils.py", line 2861, in sample
outputs = self(
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\transformers\models\mixtral\modeling_mixtral.py", line 1213, in forward
outputs = self.model(
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\transformers\models\mixtral\modeling_mixtral.py", line 1081, in forward
layer_outputs = decoder_layer(
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\transformers\models\mixtral\modeling_mixtral.py", line 810, in forward
hidden_states, router_logits = self.block_sparse_moe(hidden_states)
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\transformers\models\mixtral\modeling_mixtral.py", line 708, in forward
router_logits = self.gate(hidden_states)
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "D:\booga\text-generation-webui\installer_files\env\lib\site-packages\auto_gptq\nn_modules\qlinear\qlinear_cuda_old.py", line 239, in forward
zeros = zeros.reshape(-1, 1, zeros.shape[1] * zeros.shape[2])
RuntimeError: cannot reshape tensor of 0 elements into shape [-1, 1, 0] because the unspecified dimension size -1 can be any value and is ambiguous

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