Instructions to use augmxnt/shisa-7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use augmxnt/shisa-7b-v1 with Transformers:

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

pipe = pipeline("text-generation", model="augmxnt/shisa-7b-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("augmxnt/shisa-7b-v1")
model = AutoModelForCausalLM.from_pretrained("augmxnt/shisa-7b-v1")
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 augmxnt/shisa-7b-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "augmxnt/shisa-7b-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "augmxnt/shisa-7b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/augmxnt/shisa-7b-v1

SGLang

How to use augmxnt/shisa-7b-v1 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 "augmxnt/shisa-7b-v1" \
    --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": "augmxnt/shisa-7b-v1",
		"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 "augmxnt/shisa-7b-v1" \
        --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": "augmxnt/shisa-7b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use augmxnt/shisa-7b-v1 with Docker Model Runner:
```
docker model run hf.co/augmxnt/shisa-7b-v1
```

Quants

by leonardlin - opened Dec 8, 2023

Discussion

leonardlin

AUGMXNT org Dec 8, 2023

•

edited Dec 10, 2023

Thanks @TheBloke for doing his thing :)

I'll keep this list updated if GGUFs come along (See https://huggingface.co/augmxnt/shisa-7b-v1/discussions/1 to follow along on that story, basically llama.cpp bugged atm for most BPE tokenizers so no point in quantizing).

It looks like mmnga was able to get GGUF conversion working with a custom_shisa.py conversion script that combines the extra BPE characters into the spm tokenizer. Seems to run great, thanks!

GGUF: https://huggingface.co/mmnga/shisa-7b-v1-gguf

If anyone does their own (EXLs, etc) feel free to post it in here.

leonardlin

AUGMXNT org Dec 12, 2023

I noted while clicking around that @LoneStriker made some EXL2 quants (thanks!):

Note, also of interest, while I was doing inference benchmarking, I also created an H6 4.63BPW quant to match the BPW of @mmnga 's q4_K_M GGUF: https://huggingface.co/augmxnt/shisa-7b-v1-exl2-h6-4.63bpw

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