Instructions to use bobotsalos/fingpt_lora_crypto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use bobotsalos/fingpt_lora_crypto with PEFT:
```
Task type is invalid.
```

How to use bobotsalos/fingpt_lora_crypto with Transformers:

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

pipe = pipeline("text-generation", model="bobotsalos/fingpt_lora_crypto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bobotsalos/fingpt_lora_crypto", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bobotsalos/fingpt_lora_crypto with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/bobotsalos/fingpt_lora_crypto

SGLang

How to use bobotsalos/fingpt_lora_crypto 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 "bobotsalos/fingpt_lora_crypto" \
    --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": "bobotsalos/fingpt_lora_crypto",
		"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 "bobotsalos/fingpt_lora_crypto" \
        --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": "bobotsalos/fingpt_lora_crypto",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bobotsalos/fingpt_lora_crypto with Docker Model Runner:
```
docker model run hf.co/bobotsalos/fingpt_lora_crypto
```

fingpt_lora_crypto / tokenizer_config.json

bobotsalos

Llama-3.1-8B-Instruct Lora fine runing on cidl news articles dataset

b11935c 7 days ago

download

history blame contribute delete

354 Bytes

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 088f9b96f862e193d00549fd34e6ec594954294456faf95b1ab660cc54ed253b
Size of remote file:: 354 Bytes
SHA256:: 58d954db45152000b3596db19f22648213abd8a65893610380e527b7c87802b6

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.