Instructions to use davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora with Transformers:

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

pipe = pipeline("text-generation", model="davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora

SGLang

How to use davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora 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 "davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora" \
    --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": "davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora",
		"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 "davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora" \
        --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": "davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora with Docker Model Runner:
```
docker model run hf.co/davidkim205/Mistral-7B-Instruct-v0.2-theme_15k-sft-lora
```

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Mistral-7B-Instruct-v0.2-theme_15k-sft-lora

Model Details

Mistral-7B-Instruct-v0.2-theme_15k-sft-lora is an advanced text generation AI model created by 2digit with a specific focus on news analysis.

Stock-Related Theme Recognition: This model excels in identifying themes and topics relevant to the stock market. It efficiently detects news related to market trends, investment strategies, regulatory changes, and other stock market content. By categorizing articles based on these themes, Mistral-7B-Instruct-v0.2-theme_15k-sft-lora aids analysts and investors in staying updated on pertinent market developments.

Advantages:

Efficiency: Mistral-7B-Instruct-v0.2-theme_15k-sft-lora streamlines the workflow for news analysis. It automatically extracts critical information from news articles, facilitating quicker insights for financial analysts, journalists, market researchers, and investors.
Stock Grouping Capability: Leveraging the identified themes, the model enables the grouping of stocks. By categorizing stocks into relevant groups based on extracted themes, Mistral-7B-Instruct-v0.2-theme_15k-sft-lora offers a structured approach to market analysis. This functionality aids in focused analysis, comparison within thematic clusters, and assists in portfolio management and investment strategy formulation.

Overall, Mistral-7B-Instruct-v0.2-theme_15k-sft-lora stands as a robust solution for individuals engaged in news analysis, offering timely insights and facilitating informed decision-making in the dynamic landscape of the stock market.

License

Use of this model requires company approval. Please contact AI@2digit.io. For more details, please refer to the website below: https://2digit.io/#contactus

Dataset

The model was trained on an internal dataset from 2digit, consisting of 15k dataset.

size	description
15,473	Human-labeled theme stock dataset

Evaluation

We measured model accuracy through an internal evaluation system.

task	accuracy	description
theme	0.91	Extract themes and related companies

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Collection

These are models specifically designed for financial use used by 2digit. These models can only be used after approval from the 2digit company. • 8 items • Updated Mar 2