Instructions to use ausboss/llama-30b-supercot-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ausboss/llama-30b-supercot-4bit with Transformers:

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

pipe = pipeline("text-generation", model="ausboss/llama-30b-supercot-4bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ausboss/llama-30b-supercot-4bit")
model = AutoModelForCausalLM.from_pretrained("ausboss/llama-30b-supercot-4bit")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ausboss/llama-30b-supercot-4bit with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ausboss/llama-30b-supercot-4bit

SGLang

How to use ausboss/llama-30b-supercot-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 "ausboss/llama-30b-supercot-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": "ausboss/llama-30b-supercot-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 "ausboss/llama-30b-supercot-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": "ausboss/llama-30b-supercot-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ausboss/llama-30b-supercot-4bit with Docker Model Runner:
```
docker model run hf.co/ausboss/llama-30b-supercot-4bit
```

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Check out the documentation for more information.

Merge of huggyllama/llama-30b + kaiokendev/SuperCOT-LoRA

Supercot was trained to work with langchain prompting.

Load up locally in my custom LLM notebook that uses the Oobabooga modules to load up models: https://github.com/ausboss/Local-LLM-Langchain

Then you can add cells from of these other notebooks for testing: https://github.com/gkamradt/langchain-tutorials

From kaiokendev Lora page

Compatibility

This LoRA is compatible with any 7B, 13B or 30B 4-bit quantized LLaMa model, including ggml quantized converted bins

Prompting

You should prompt the LoRA the same way you would prompt Alpaca or Alpacino:

Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
<instruction>

### Input:
<any additional context. Remove this if it's not neccesary>

### Response:
<make sure to leave a single new-line here for optimal results>

Remember that with lower parameter sizes, the structure of the prompt becomes more important. The same prompt worded differently can give wildly different answers. Consider using the following suggestion suffixes to improve output quality:

"Think through this step by step"
"Let's think about this logically"
"Explain your reasoning"
"Provide details to support your answer"
"Compare and contrast your answer with alternatives"

Coming Soon

Tweet fix for 13B and 7B - lower model sizes seem to be extremely sensitive to hashtags at the end of training data responses, especially at longer cutoffs

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