Instructions to use abacusai/Smaug-Mixtral-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abacusai/Smaug-Mixtral-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="abacusai/Smaug-Mixtral-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abacusai/Smaug-Mixtral-v0.1")
model = AutoModelForCausalLM.from_pretrained("abacusai/Smaug-Mixtral-v0.1", 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 abacusai/Smaug-Mixtral-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abacusai/Smaug-Mixtral-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abacusai/Smaug-Mixtral-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abacusai/Smaug-Mixtral-v0.1

SGLang

How to use abacusai/Smaug-Mixtral-v0.1 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 "abacusai/Smaug-Mixtral-v0.1" \
    --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": "abacusai/Smaug-Mixtral-v0.1",
		"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 "abacusai/Smaug-Mixtral-v0.1" \
        --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": "abacusai/Smaug-Mixtral-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use abacusai/Smaug-Mixtral-v0.1 with Docker Model Runner:
```
docker model run hf.co/abacusai/Smaug-Mixtral-v0.1
```

GGUF Quantisations

by smcleod - opened Feb 21, 2024

Discussion

smcleod

Feb 21, 2024

•

edited Feb 21, 2024

I've had a crack (for the first time) at quantising models with Q3_K_M and Q4_K_M GGUF variants if anyone finds them useful, I've also pushed these to Ollama's model registry.

Disclaimer - I literally read how to quantise models yesterday so while I think it went to plan - please do let me know if there are any issues!

fermuch

Mar 13, 2024

It doesn't seem to be working. The output seems to be garbage. Did it work for you?

deleted

Mar 14, 2024

•

This comment has been hidden

gobean

May 5, 2024

I added some more quants: https://huggingface.co/gobean/Smaug-Mixtral-v0.1-GGUF, using llama.cpp from 4/18/2024.

Mixtral Instruct worked better for me with qx_0 for me so I used those - unsure why qx_k_y behave differently. Output tests on few shot seem good, q4_0 is fast enough for regular use with ~18gb vram usage.

smcleod changed discussion status to closed May 13, 2024

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