Instructions to use Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled with Transformers:

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

pipe = pipeline("text-generation", model="Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled")
model = AutoModelForCausalLM.from_pretrained("Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled", 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 Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled

SGLang

How to use Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled 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 "Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled" \
    --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": "Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled",
		"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 "Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled" \
        --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": "Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled with Docker Model Runner:
```
docker model run hf.co/Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mixtral-8x7B-Instruct-v0.1-upscaled

This is a frankenmerge of mistralai/Mixtral-8x7B-Instruct-v0.1 created by interleaving layers of itself using mergekit.

Benchmark

The benchmark score of the mt-bench for this model and the original models are as follows:

1-turn

Model	Size	Coding	Extraction	Humanities	Math	Reasoning	Roleplay	STEM	Writing	avg_score
Mixtral-8x7B-Instruct-v0.1	8x7B	5.3	8.5	9.9	6.8	6.0	9.1	9.55	8.9	8.00625
This model	around 8x12B?	6.3	8.4	9.9	5.4	7.7	9.2	9.75	9.8	8.30625

2-turn

Model	Size	Coding	Extraction	Humanities	Math	Reasoning	Roleplay	STEM	Writing	avg_score
Mixtral-8x7B-Instruct-v0.1	8x7B	4.1	8.4	9.8	4.7	5.6	9.0	9.2	9.5	7.5375
This model	around 8x12B?	4.2	7.4	9.9	4.0	5.2	9.5	8.7	8.0	7.1125

Merge Details

Merge Method

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

mistralai/Mixtral-8x7B-Instruct-v0.1

Configuration

The following YAML configuration was used to produce this model:

merge_method: passthrough
slices:
  - sources:
      - model: mistralai/Mixtral-8x7B-Instruct-v0.1
        layer_range: [0, 8]
  - sources:
      - model: mistralai/Mixtral-8x7B-Instruct-v0.1
        layer_range: [4, 12]
  - sources:
      - model: mistralai/Mixtral-8x7B-Instruct-v0.1
        layer_range: [8, 16]
  - sources:
      - model: mistralai/Mixtral-8x7B-Instruct-v0.1
        layer_range: [12, 20]
  - sources:
      - model: mistralai/Mixtral-8x7B-Instruct-v0.1
        layer_range: [16, 24]
  - sources:
      - model: mistralai/Mixtral-8x7B-Instruct-v0.1
        layer_range: [20, 28]
  - sources:
      - model: mistralai/Mixtral-8x7B-Instruct-v0.1
        layer_range: [24, 32]
dtype: bfloat16
tokenizer_source: base

Downloads last month: 73

Safetensors

Model size

82B params

Tensor type

BF16

Model tree for Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled

Base model

mistralai/Mixtral-8x7B-v0.1

Finetuned

mistralai/Mixtral-8x7B-Instruct-v0.1

Finetuned

(76)

this model

Quantizations

2 models