Image-Text-to-Text
Transformers
Safetensors
English
qwen3_5
trading
options
options-trading
day-trading
swing-trading
penny-stocks
finance
quantitative-finance
algorithmic-trading
vpa
volume-price-analysis
vision-language
vision-language-model
vlm
multimodal
multimodal-trading
chart-analysis
chart-pattern-recognition
technical-analysis
qwen
qwen3
qwen3.5
qwen3-vl
lora
fp8
vllm
tradier
decision-making
local-first
desktop-app
consumer-gpu
5070ti
behavioral-finance
backtesting
conversational
Instructions to use tjarvis91/vfai-x-3.5-9b-options with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tjarvis91/vfai-x-3.5-9b-options with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="tjarvis91/vfai-x-3.5-9b-options") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("tjarvis91/vfai-x-3.5-9b-options") model = AutoModelForMultimodalLM.from_pretrained("tjarvis91/vfai-x-3.5-9b-options") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tjarvis91/vfai-x-3.5-9b-options with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tjarvis91/vfai-x-3.5-9b-options" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tjarvis91/vfai-x-3.5-9b-options", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/tjarvis91/vfai-x-3.5-9b-options
- SGLang
How to use tjarvis91/vfai-x-3.5-9b-options 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 "tjarvis91/vfai-x-3.5-9b-options" \ --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": "tjarvis91/vfai-x-3.5-9b-options", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "tjarvis91/vfai-x-3.5-9b-options" \ --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": "tjarvis91/vfai-x-3.5-9b-options", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use tjarvis91/vfai-x-3.5-9b-options with Docker Model Runner:
docker model run hf.co/tjarvis91/vfai-x-3.5-9b-options
Devlog 4/7 — Adaptive Compute Architecture: Sparse cognition, latent reasoning, and the right to think harder
#13 opened 5 days ago
by
tjarvis91
Devlog 3/7 — Shell-Governed Cognition: When the wrapper around the model turned out to be the moat
#12 opened 5 days ago
by
tjarvis91
VFAi-X Qovaryx 2.5.2 hotfix is live
#11 opened 10 days ago
by
tjarvis91
App Showcase -- see the desktop app before you install
1
#10 opened 15 days ago
by
tjarvis91
Franken-B is the new VFAi-X flagship — live now (V3.7 + V6 Sniper opt-in)
1
#9 opened 18 days ago
by
tjarvis91
V6: the sniper that matches V3.7 per-trade profitability at 2% of its trade count
1
#8 opened 19 days ago
by
tjarvis91
Devlog 2/7 — Legacy Brain Crystallization: Training the law before the noise
1
#7 opened 19 days ago
by
tjarvis91
V6 candidate update: correcting regressions since V3.7
1
#6 opened 20 days ago
by
tjarvis91
Devlog 1/7 — AI Without Big Data Centers: Notes from a frontier built on a single consumer GPU
1
#5 opened 22 days ago
by
tjarvis91
Why Financial AI Needs Better Evaluation Before Bigger Models
1
#4 opened 22 days ago
by
tjarvis91
Building time-safe financial AI: data integrity first
1
#3 opened 22 days ago
by
tjarvis91
V5.0 In Training: Fresh Base, Real-World Selectivity
1
#2 opened 23 days ago
by
tjarvis91
V3.7 Evaluation Report: Real Backtest Numbers, With Caveats
1
#1 opened 23 days ago
by
tjarvis91