Instructions to use rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03 with Transformers:

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

pipe = pipeline("text-generation", model="rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03")
model = AutoModelForMultimodalLM.from_pretrained("rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03

SGLang

How to use rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03 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 "rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03" \
    --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": "rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03",
		"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 "rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03" \
        --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": "rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03 with Docker Model Runner:
```
docker model run hf.co/rsh345/llama3-8b-finance-elyza-linear-a_w07-b_w03
```

llama3-8b-finance-elyza-linear-a_w07-b_w03

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Linear merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
- model: elyza/Llama-3-ELYZA-JP-8B
  parameters:
    weight: 0.7
- model: instruction-pretrain/finance-Llama3-8B
  parameters:
    weight: 0.3
merge_method: linear
dtype: float16