Instructions to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf with Transformers:

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

pipe = pipeline("text-generation", model="ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf")
model = AutoModelForCausalLM.from_pretrained("ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf

SGLang

How to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf 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 "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf" \
    --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": "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf",
		"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 "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf" \
        --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": "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf with Docker Model Runner:
```
docker model run hf.co/ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf
```

NameError: name 'flash_attn_func' is not defined

by Neman - opened Feb 16, 2024

Discussion

Neman

Feb 16, 2024

•

edited Feb 16, 2024

I have flash-attn installed (v 2.5.2), but I get:
Exception has occurred: NameError
name 'flash_attn_func' is not defined
File "/home/neman/.cache/huggingface/modules/transformers_modules/BlackSamorez_Mixtral-8x7b-AQLM-2Bit-1x16-hf/modeling_mixtral_aqlm.py", line 65, in
_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
File "/home/neman/PROGRAMMING/PYTHON/DuckDuckGo_Search/AQLM_test1.py", line 3, in
quantized_model = AutoModelForCausalLM.from_pretrained(
NameError: name 'flash_attn_func' is not defined

toy code:
from transformers import AutoTokenizer, AutoModelForCausalLM
quantized_model = AutoModelForCausalLM.from_pretrained(
"/mnt/disk2/LLM_MODELS/models/BlackSamorez_Mixtral-8x7b-AQLM-2Bit-1x16-hf",
torch_dtype="auto", device_map="auto", low_cpu_mem_usage=True,
)
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
output = quantized_model.generate(tokenizer("", return_tensors="pt")["input_ids"].cuda(), max_new_tokens=10)
output = quantized_model.generate(tokenizer("I'm AQLM, ", return_tensors="pt")["input_ids"].cuda(), min_new_tokens=128, max_new_tokens=128)
print(tokenizer.decode(output[0]))

Any ideas?

UPDATE:
I checked and saw that from few days ago there is flash-attn 2.5.3 so I updated. Now I get different error:
Exception has occurred: RuntimeError
Only Tensors of floating point and complex dtype can require gradients
File "/home/neman/.cache/huggingface/modules/transformers_modules/BlackSamorez_Mixtral-8x7b-AQLM-2Bit-1x16-hf/modeling_mixtral_aqlm.py", line 308, in init
self.q_proj = QuantizedLinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, **config.aqlm)
File "/home/neman/.cache/huggingface/modules/transformers_modules/BlackSamorez_Mixtral-8x7b-AQLM-2Bit-1x16-hf/modeling_mixtral_aqlm.py", line 889, in init
self.self_attn = MIXTRAL_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
File "/home/neman/.cache/huggingface/modules/transformers_modules/BlackSamorez_Mixtral-8x7b-AQLM-2Bit-1x16-hf/modeling_mixtral_aqlm.py", line 1093, in
[MixtralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
File "/home/neman/.cache/huggingface/modules/transformers_modules/BlackSamorez_Mixtral-8x7b-AQLM-2Bit-1x16-hf/modeling_mixtral_aqlm.py", line 1093, in init
[MixtralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
File "/home/neman/.cache/huggingface/modules/transformers_modules/BlackSamorez_Mixtral-8x7b-AQLM-2Bit-1x16-hf/modeling_mixtral_aqlm.py", line 1277, in init
self.model = MixtralModel(config)
File "/home/neman/PROGRAMMING/PYTHON/DuckDuckGo_Search/AQLM_test1.py", line 3, in
quantized_model = AutoModelForCausalLM.from_pretrained(
RuntimeError: Only Tensors of floating point and complex dtype can require gradients

BlackSamorez

IST Austria Distributed Algorithms and Systems Lab org Feb 16, 2024

Installing the latest accelerate will fix the second error.

Neman

Feb 16, 2024

It did. Thank you for support.

BlackSamorez changed discussion status to closed Feb 16, 2024

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