Instructions to use pran1805/FalconFinance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pran1805/FalconFinance with Transformers:

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

pipe = pipeline("text-generation", model="pran1805/FalconFinance", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pran1805/FalconFinance", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("pran1805/FalconFinance", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pran1805/FalconFinance with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pran1805/FalconFinance"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pran1805/FalconFinance",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pran1805/FalconFinance

SGLang

How to use pran1805/FalconFinance 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 "pran1805/FalconFinance" \
    --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": "pran1805/FalconFinance",
		"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 "pran1805/FalconFinance" \
        --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": "pran1805/FalconFinance",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pran1805/FalconFinance with Docker Model Runner:
```
docker model run hf.co/pran1805/FalconFinance
```

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Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model ="pran1805/FalconFinance"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

sequences = pipeline(
   "QUERY",
    max_length=80,
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
    print(f"Result: {seq['generated_text'].split('Below')[0]}")

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Safetensors

Model size

1B params

Tensor type

BF16

pran1805
/

FalconFinance

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Usage

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