Instructions to use tunedtensor/qwen3.5-2b-financial-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tunedtensor/qwen3.5-2b-financial-sentiment with Transformers:

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

pipe = pipeline("text-generation", model="tunedtensor/qwen3.5-2b-financial-sentiment")
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("tunedtensor/qwen3.5-2b-financial-sentiment")
model = AutoModelForMultimodalLM.from_pretrained("tunedtensor/qwen3.5-2b-financial-sentiment")
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 tunedtensor/qwen3.5-2b-financial-sentiment with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tunedtensor/qwen3.5-2b-financial-sentiment"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tunedtensor/qwen3.5-2b-financial-sentiment",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tunedtensor/qwen3.5-2b-financial-sentiment

SGLang

How to use tunedtensor/qwen3.5-2b-financial-sentiment 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 "tunedtensor/qwen3.5-2b-financial-sentiment" \
    --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": "tunedtensor/qwen3.5-2b-financial-sentiment",
		"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 "tunedtensor/qwen3.5-2b-financial-sentiment" \
        --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": "tunedtensor/qwen3.5-2b-financial-sentiment",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tunedtensor/qwen3.5-2b-financial-sentiment with Docker Model Runner:
```
docker model run hf.co/tunedtensor/qwen3.5-2b-financial-sentiment
```

Qwen3.5-2B Financial Sentiment Extractor

This model is a Tuned Tensor fine-tune of Qwen/Qwen3.5-2B for extracting a structured market sentiment signal from finance-related tweets and social posts.

Given one post, it returns compact JSON:

{"sentiment":"bearish","label":0,"rationale":"The post expresses a bearish market signal."}

Label mapping:

0: bearish
1: bullish
2: neutral

Training

Base model: Qwen/Qwen3.5-2B
Training dataset: zeroshot/twitter-financial-news-sentiment
Dataset license: MIT
Base model license: Apache-2.0
Tuned Tensor run ID: 61d64e3e-b9a1-48e5-8803-c7c30a4df5a8
Tuned Tensor model ID: fd09ae23-1935-48b4-b838-9e563df59b49
Training rows used by trainer: 4,080
Precision: bf16
Epochs: 1
Final reported training loss: 0.5898758276

The source dataset was converted into strict input / output supervised rows with balanced labels. The behavior spec used during training is included as tunedtensor.json.

Evaluation

Tuned Tensor LLM-judge evaluation, capped at 120 examples per split:

Split	Base avg score	Tuned avg score	Delta	Base pass rate	Tuned pass rate	Delta
Validation	0.819	0.903	+0.084	79.2%	86.7%	+7.5 pp
Test	0.834	0.875	+0.041	80.0%	85.8%	+5.8 pp

Output format diagnostics:

Valid JSON: 100%
Strict JSON: 100%
Expected schema keys: 100%
Non-JSON prefix: 0%

Local hand-curated smoke tests are included:

local_real_tests_fd09ae23.json
local_real_tests_fd09ae23_batch2.json

Usage

The model was locally served and tested with Tuned Tensor's OpenAI-compatible serving runtime:

tt models serve fd09ae23-1935-48b4-b838-9e563df59b49 \
  --spec tunedtensor.json \
  --device auto \
  --temperature 0 \
  --max-tokens 96

Example prompt:

Extract the market sentiment signal from this finance-related social post. Return only strict JSON with exactly these keys: sentiment, label, rationale. sentiment must be one of bearish, bullish, neutral; label must be one of 0, 1, 2.

Post: $NVDA shares jump after analysts raise price targets on stronger AI chip demand.

Expected response shape:

{"sentiment":"bullish","label":1,"rationale":"The post expresses a bullish market signal."}

Limitations

This model classifies short market-social posts into coarse sentiment categories. It is not an investment advisor, trading system, or factual market-data source.

Known caveat from evaluation: some ambiguous mixed-signal posts may still be difficult, especially when a post contains both a clearly negative primary event and a secondary contrarian or factual framing.

Downloads last month: -

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for tunedtensor/qwen3.5-2b-financial-sentiment

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Qwen/Qwen3.5-2B-Base

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Qwen/Qwen3.5-2B

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qwen3.5-2b-financial-sentiment