Instructions to use HachiML/Mists-7B-v0.1-not-trained-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HachiML/Mists-7B-v0.1-not-trained-test with Transformers:

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

pipe = pipeline("text-generation", model="HachiML/Mists-7B-v0.1-not-trained-test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("HachiML/Mists-7B-v0.1-not-trained-test", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HachiML/Mists-7B-v0.1-not-trained-test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HachiML/Mists-7B-v0.1-not-trained-test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HachiML/Mists-7B-v0.1-not-trained-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HachiML/Mists-7B-v0.1-not-trained-test

SGLang

How to use HachiML/Mists-7B-v0.1-not-trained-test 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 "HachiML/Mists-7B-v0.1-not-trained-test" \
    --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": "HachiML/Mists-7B-v0.1-not-trained-test",
		"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 "HachiML/Mists-7B-v0.1-not-trained-test" \
        --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": "HachiML/Mists-7B-v0.1-not-trained-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HachiML/Mists-7B-v0.1-not-trained-test with Docker Model Runner:
```
docker model run hf.co/HachiML/Mists-7B-v0.1-not-trained-test
```

Mists-7B-v0.1-not-trained

Mists(Mistral Time Series) model is a multimodal model that combines language and time series model.
This model is based on the following models:

This is an experimental model.
It has some limitations and is not suitable for use at this time.

How to load model

!pip install git+https://github.com/Hajime-Y/moment.git
!pip install -U transformers
!git clone https://github.com/Hajime-Y/Mists.git

import torch

from Mists.configuration_mists import MistsConfig
from Mists.modeling_mists import MistsForConditionalGeneration
from Mists.processing_mists import MistsProcessor

model_id = "HachiML/Mists-7B-v0.1-not-trained"
model = MistsForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
).to("cuda")
processor = MistsProcessor.from_pretrained(model_id)

import pandas as pd

hist_ndaq_512 = pd.DataFrame("nasdaq_price_history.csv")
time_series_data = torch.tensor(hist_ndaq_512[["Open", "High", "Low", "Close", "Volume"]].values, dtype=torch.float)
time_series_data = time_series_data.t().unsqueeze(0)

prompt = "USER: <time_series>\nWhat are the features of this data?\nASSISTANT:"
inputs = processor(prompt, time_series_data, return_tensors='pt').to(torch.float32)

output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0], skip_special_tokens=True))

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Safetensors

Model size

8B params

Tensor type

F32