Instructions to use Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit with Transformers:

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

pipe = pipeline("text-generation", model="Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit")
model = AutoModelForCausalLM.from_pretrained("Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit

SGLang

How to use Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit 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 "Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit" \
    --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": "Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit",
		"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 "Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit" \
        --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": "Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit with Docker Model Runner:
```
docker model run hf.co/Monero/WizardLM-SuperCOT-StoryTelling-30b-4bit
```

4090 test with OobaBooga (In Windows) fails to load the model

by cleverest - opened Jun 4, 2023

Discussion

cleverest

Jun 4, 2023

IDEAS on how to fix this?

I get this error:

Traceback (most recent call last): File “C:\Users\cleverest\oobabooga_windows\text-generation-webui\server.py”, line 68, in load_model_wrapper shared.model, shared.tokenizer = load_model(shared.model_name) File “C:\Users\cleverest\oobabooga_windows\text-generation-webui\modules\models.py”, line 95, in load_model output = load_func(model_name) File “C:\Users\cleverest\oobabooga_windows\text-generation-webui\modules\models.py”, line 275, in GPTQ_loader model = modules.GPTQ_loader.load_quantized(model_name) File “C:\Users\cleverest\oobabooga_windows\text-generation-webui\modules\GPTQ_loader.py”, line 177, in load_quantized model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, kernel_switch_threshold=threshold) File “C:\Users\cleverest\oobabooga_windows\text-generation-webui\modules\GPTQ_loader.py”, line 84, in _load_quant model.load_state_dict(safe_load(checkpoint), strict=False) File “C:\Users\cleverest\oobabooga_windows\installer_files\env\lib\site-packages\torch\nn\modules\module.py”, line 2041, in load_state_dict raise RuntimeError(‘Error(s) in loading state_dict for {}:\n\t{}’.format( RuntimeError: Error(s) in loading state_dict for LlamaForCausalLM: size mismatch for model.layers.0.self_attn.k_proj.qzeros: copying a param with shape torch.Size([1, 832]) from checkpoint, the shape in current model is torch.Size([52, 832]). size mismatch for model.layers.0.self_attn.k_proj.scales: copying a param with shape torch.Size([1, 6656]) from checkpoint, the shape in current model is torch.Size([52, 6656]). size mismatch for model.layers.0.self_attn.o_proj.qzeros: copying a param with shape torch.Size([1, 832]) from checkpoint, the shape in current model is torch.Size([52, 832]). size mismatch for model.layers.0.self_attn.o_proj.scales: copying a param with shape torch.Size([1, 6656]) from checkpoint, the shape in current model is torch.Size([52, 6656]). size mismatch for model.layers.0.self_attn.q_proj.qzeros: copying a param with shape torch.Size([1, 832]) from checkpoint, the shape in current model is torch.Size([52, 832]). size mismatch for

Monero

Owner Jun 4, 2023

I've seen similar errors when the group size wasn't set correctly, make sure it's set to 128

cleverest

Jun 4, 2023

Ah the name didn't have 128 in it so I didn't even bother... I left home, I'll try it later, thanks

Monero

Owner Jun 4, 2023

Ah the name didn't have 128 in it so I didn't even bother... I left home, I'll try it later, thanks

the filename does though! :) hope it works

cleverest

Jun 5, 2023

Yup, that fixed it. Thanks! Is there any chance of getting a non-128G model of this model at some point?

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