Instructions to use AITRADER/Amsi-fin-o1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AITRADER/Amsi-fin-o1.5 with Transformers:

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

pipe = pipeline("image-text-to-text", model="AITRADER/Amsi-fin-o1.5", device_map="auto")
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("AITRADER/Amsi-fin-o1.5")
model = AutoModelForMultimodalLM.from_pretrained("AITRADER/Amsi-fin-o1.5", device_map="auto")
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 AITRADER/Amsi-fin-o1.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AITRADER/Amsi-fin-o1.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AITRADER/Amsi-fin-o1.5",
		"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/AITRADER/Amsi-fin-o1.5

SGLang

How to use AITRADER/Amsi-fin-o1.5 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 "AITRADER/Amsi-fin-o1.5" \
    --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": "AITRADER/Amsi-fin-o1.5",
		"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 "AITRADER/Amsi-fin-o1.5" \
        --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": "AITRADER/Amsi-fin-o1.5",
		"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 AITRADER/Amsi-fin-o1.5 with Docker Model Runner:
```
docker model run hf.co/AITRADER/Amsi-fin-o1.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Amsi-fin-o1.5

This release replaces the previous weak AITRADER finance model line with a full-parameter Qwen 3.5 domain update focused on trading strategy reasoning, market structure interpretation, risk framing, and finance question answering.

What Changed

Base model: Qwen/Qwen3.5-9B-Base
Fine-tuning mode: full_finetuning=true
Optimizer: adafactor
Learning rate: 1e-05
Sequence length: 1024
Gradient accumulation: 4
Source artifact used for packaging: full
Packaging date: 2026-03-15

Intended Behavior

Stronger text-only finance and trading strategy reasoning than the prior AITRADER/Amsi-fin-o1 line.
Better regime, invalidation, and risk explanation behavior.
Cleaner supervised tuning footprint for OpenAI-compatible serving and downstream benchmarking.

Local Benchmark Results

Pending. Run bash scripts/run_release_benchmarks.sh after training completes.

Public Comparison References

Model	Benchmark	Public score	Note	Source
AITRADER/Amsi-fin-o1	Hugging Face model card	-	Vision-language 4B predecessor; no public FinEval or BizFinBench score published in the model card.	https://huggingface.co/AITRADER/Amsi-fin-o1
Qwen2.5-7B-Instruct	FinEval text weighted average	69.7	Public FinEval text leaderboard score.	https://github.com/SUFE-AIFLM-Lab/FinEval
GLM-4-9B-Chat	FinEval Chinese financial weighted average	58.4	Public FinEval Chinese financial domain leaderboard score.	https://github.com/SUFE-AIFLM-Lab/FinEval
FinGPTv3.1	FinEval Chinese financial weighted average	27.1	Public FinEval Chinese financial domain leaderboard score.	https://github.com/SUFE-AIFLM-Lab/FinEval
GPT-4o	BizFinBench average	71.8	Public BizFinBench v1 average score.	https://github.com/HiThink-Research/BizFinBench
ChatGPT-o3	BizFinBench average	73.86	Public BizFinBench v1 average score.	https://github.com/HiThink-Research/BizFinBench

These public figures come from external leaderboards and model cards. They are not apples-to-apples with this run unless the same benchmark protocol and prompt format are used. They are included as reference baselines for the later release note.

Training Configuration Snapshot

base_model: Qwen/Qwen3.5-9B-Base
max_seq_length: 1024
full_finetuning: True
gradient_checkpointing: True
learning_rate: 1e-05
weight_decay: 0.01
per_device_train_batch_size: 1
gradient_accumulation_steps: 4
warmup_steps: 100
max_steps: 12000
optim: adafactor
seed: 3407

Release Workflow

Finish training and confirm the final checkpoint or full/ export.
Run bash scripts/run_release_benchmarks.sh.
Run bash scripts/prepare_hf_update.sh.
Review the generated bundle under artifacts/releases/.
Push the bundle contents to AITRADER/Amsi-fin-o1.5 when satisfied.

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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for AITRADER/Amsi-fin-o1.5

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

(124)

this model

Finetunes

1 model

Quantizations

3 models