Instructions to use sll0107/Qwen3-8B-finance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sll0107/Qwen3-8B-finance with Transformers:

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

pipe = pipeline("text-generation", model="sll0107/Qwen3-8B-finance")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sll0107/Qwen3-8B-finance")
model = AutoModelForCausalLM.from_pretrained("sll0107/Qwen3-8B-finance")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sll0107/Qwen3-8B-finance with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sll0107/Qwen3-8B-finance"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sll0107/Qwen3-8B-finance",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sll0107/Qwen3-8B-finance

SGLang

How to use sll0107/Qwen3-8B-finance 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 "sll0107/Qwen3-8B-finance" \
    --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": "sll0107/Qwen3-8B-finance",
		"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 "sll0107/Qwen3-8B-finance" \
        --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": "sll0107/Qwen3-8B-finance",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sll0107/Qwen3-8B-finance with Docker Model Runner:
```
docker model run hf.co/sll0107/Qwen3-8B-finance
```

Qwen3-8B-finance

Qwen3-8B fine-tuned for multilingual financial NLP (sentiment analysis, topic classification, question answering, summarization, and multiple-choice).

Inference notes

Decoding: greedy (temperature=0, top_p=1).
Prompt format: the model is trained to receive the prompt verbatim — no system prompt and no chat wrapping. The embedded chat_template (in tokenizer_config.json) is intentionally pass-through for this reason.

Running with ollama (HF → GGUF → ollama)

Convert to GGUF (f16), then create the ollama model with a pass-through template so the prompt is fed as-is (this avoids a default chat template reshaping the input):

# Modelfile
FROM ./model-f16.gguf
PARAMETER temperature 0
PARAMETER top_p 1
PARAMETER seed 42
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
TEMPLATE """{{ .Prompt }}"""

The same snippet is provided in template.tmpl.

Downloads last month: 42

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for sll0107/Qwen3-8B-finance

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

(1931)

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