Instructions to use finansai/BIST-Financial-Qwen-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use finansai/BIST-Financial-Qwen-7B with Transformers:

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

pipe = pipeline("text-generation", model="finansai/BIST-Financial-Qwen-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("finansai/BIST-Financial-Qwen-7B")
model = AutoModelForCausalLM.from_pretrained("finansai/BIST-Financial-Qwen-7B")
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]:]))

MLX

How to use finansai/BIST-Financial-Qwen-7B with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("finansai/BIST-Financial-Qwen-7B")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

llama-cpp-python

How to use finansai/BIST-Financial-Qwen-7B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="finansai/BIST-Financial-Qwen-7B",
	filename="gguf/qwen-kap-final-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use finansai/BIST-Financial-Qwen-7B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf finansai/BIST-Financial-Qwen-7B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf finansai/BIST-Financial-Qwen-7B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf finansai/BIST-Financial-Qwen-7B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf finansai/BIST-Financial-Qwen-7B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf finansai/BIST-Financial-Qwen-7B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf finansai/BIST-Financial-Qwen-7B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf finansai/BIST-Financial-Qwen-7B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf finansai/BIST-Financial-Qwen-7B:Q4_K_M

Use Docker

docker model run hf.co/finansai/BIST-Financial-Qwen-7B:Q4_K_M

LM Studio
Jan

vLLM

How to use finansai/BIST-Financial-Qwen-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "finansai/BIST-Financial-Qwen-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "finansai/BIST-Financial-Qwen-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/finansai/BIST-Financial-Qwen-7B:Q4_K_M

SGLang

How to use finansai/BIST-Financial-Qwen-7B 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 "finansai/BIST-Financial-Qwen-7B" \
    --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": "finansai/BIST-Financial-Qwen-7B",
		"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 "finansai/BIST-Financial-Qwen-7B" \
        --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": "finansai/BIST-Financial-Qwen-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use finansai/BIST-Financial-Qwen-7B with Ollama:
```
ollama run hf.co/finansai/BIST-Financial-Qwen-7B:Q4_K_M
```

Unsloth Studio new

How to use finansai/BIST-Financial-Qwen-7B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for finansai/BIST-Financial-Qwen-7B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for finansai/BIST-Financial-Qwen-7B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for finansai/BIST-Financial-Qwen-7B to start chatting

Pi new

How to use finansai/BIST-Financial-Qwen-7B with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "finansai/BIST-Financial-Qwen-7B"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "finansai/BIST-Financial-Qwen-7B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use finansai/BIST-Financial-Qwen-7B with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "finansai/BIST-Financial-Qwen-7B"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default finansai/BIST-Financial-Qwen-7B

Run Hermes

hermes

MLX LM

How to use finansai/BIST-Financial-Qwen-7B with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "finansai/BIST-Financial-Qwen-7B"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "finansai/BIST-Financial-Qwen-7B"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "finansai/BIST-Financial-Qwen-7B",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use finansai/BIST-Financial-Qwen-7B with Docker Model Runner:
```
docker model run hf.co/finansai/BIST-Financial-Qwen-7B:Q4_K_M
```

Lemonade

How to use finansai/BIST-Financial-Qwen-7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull finansai/BIST-Financial-Qwen-7B:Q4_K_M

Run and chat with the model

lemonade run user.BIST-Financial-Qwen-7B-Q4_K_M

List all available models

lemonade list

Qwen KAP Turkish Financial Sentiment Model

BIST100 piyasası için özelleştirilmiş KAP (Kamuyu Aydınlatma Platformu) bildirimi analiz modeli.

Bu model, Qwen2.5-7B-Instruct üzerine iki aşamalı fine-tuning ile eğitilmiştir.

Model Bilgileri

Özellik	Değer
Base Model	Qwen/Qwen2.5-7B-Instruct
Parametre	7.6B
Dil	Türkçe
Görev	Finansal Sentiment Analizi
Quantization	4-bit (MLX), Q4_K_M (GGUF)
Context Length	32K tokens

Kullanım Alanları

KAP bildirimlerinden sentiment analizi
Hisse senedi haberlerinin değerlendirilmesi
Volatilite tahmini
İlişkili taraf işlemi tespiti
Döviz etkisi analizi
Bildirim kategorilendirmesi

İki Aşamalı Eğitim

Stage 1: Türkçe Finansal Sentiment

Dataset: 161K Türkçe finansal haber (ituperceptron/turkish-financial-sentiment)
Görev: 3 sınıflı sentiment (pozitif/nötr/negatif)
Amaç: Modele Türkçe finansal dil ve terminoloji öğretmek

Stage 2: KAP Çok Boyutlu Analiz

Dataset: 3.8K KAP bildirimi (GPT-4 etiketli) (furkanyllmz/kap-turkish-financial-sentiment)
Görev: Multi-label JSON üretimi (sentiment, volatilite, kategori vb.)
Amaç: KAP bildirimlerinden yapılandırılmış veri çıkarımı

Eğitim Parametreleri

Fine-tuning: LoRA (Low-Rank Adaptation)
Epochs: 3
Learning Rate: 1e-5
Batch Size: 4
LoRA Rank: 64
LoRA Alpha: 128

Dosyalar

MLX Format (macOS Apple Silicon)

├── config.json
├── model.safetensors
├── tokenizer.json
└── tokenizer_config.json

GGUF Format (Windows/Linux/Cross-platform)

└── gguf/
    └── qwen-kap-final-Q4_K_M.gguf (4.4 GB)

Kullanım

MLX ile (macOS)

from mlx_lm import load, generate

model, tokenizer = load("MODEL_REPO_ID")

messages = [
    {"role": "system", "content": """Sen bir KAP uzmanısın. Verilen bildirimi analiz et ve JSON formatında sonuç üret:
{
  "sentiment": <-40 ile +40 arası>,
  "volatility": <0-5>,
  "is_related_party": <0/1>,
  "currency_impact": <0/1/2>,
  "category": "<KATEGORI>"
}"""},
    {"role": "user", "content": "ASELSAN, Savunma Sanayii Başkanlığı ile 500 milyon dolarlık yeni ihale sözleşmesi imzaladı."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=prompt, max_tokens=150)
print(response)

Transformers ile

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("MODEL_REPO_ID")
tokenizer = AutoTokenizer.from_pretrained("MODEL_REPO_ID")

# ... aynı mesaj formatı

llama.cpp ile (GGUF)

./llama-cli -m qwen-kap-final-Q4_K_M.gguf \
  -p "<|im_start|>system
Sen bir KAP uzmanısın...<|im_end|>
<|im_start|>user
ASELSAN yeni ihale kazandı.<|im_end|>
<|im_start|>assistant
" -n 150

Ollama ile

# Modelfile oluştur
echo 'FROM ./qwen-kap-final-Q4_K_M.gguf' > Modelfile
ollama create qwen-kap -f Modelfile
ollama run qwen-kap

LM Studio ile

GGUF dosyasını indir
LM Studio'da "My Models" → "Import" ile ekle
Chat arayüzünde kullan

Çıktı Formatı

Model JSON formatında çıktı üretir:

{
  "sentiment": 30,
  "volatility": 2,
  "is_related_party": 0,
  "currency_impact": 2,
  "category": "YATIRIM_SOZLESME"
}

Sentiment Değerleri

Değer	Anlam
+40	Çok Olumlu (devre kesici tavan)
+30	Olumlu (kâr artışı, temettü, yeni sözleşme)
+20	Hafif Olumlu
0	Nötr
-20	Hafif Olumsuz (zarar açıklaması)
-40	Çok Olumsuz (devre kesici taban)

Volatilite Değerleri

Değer	Anlam
0	Düşük - rutin bilgilendirme
1	Normal
2	Orta
3	Yüksek
5	Çok Yüksek - devre kesici

Kategoriler

GENEL_BILGI - Rutin bilgilendirmeler
FINANSAL_RAPOR - Mali tablolar, kâr/zarar
YATIRIM_SOZLESME - Yeni yatırım, sözleşme
SERMAYE_TEMETTU - Temettü, sermaye artırımı
KAR_PAYI_DAGITIM - Kâr payı dağıtımı

Sınırlamalar

Model sadece Türkçe finansal metinler için optimize edilmiştir
Yatırım tavsiyesi niteliği taşımaz
Gerçek zamanlı piyasa verisi içermez
Tahminler %100 doğruluk garantisi vermez

Lisans

Apache 2.0

Teşekkürler

ITU Perceptron - Stage 1 eğitimi için turkish-financial-sentiment dataseti
Qwen Team - Base model

Katkıda Bulunanlar

Furkan Yılmaz - (@furkanyllmz)
Aleyna Taşdemir (@aleynatasdemir)

Referanslar

Not: Bu model araştırma ve kişisel QuantTrade algoritmalarına anlamlı feature ekleme amaçlıdır. Yatırım kararlarınızda profesyonel danışmanlık almanızı öneririz.

Downloads last month: 356

Safetensors

Model size

1B params

Tensor type

F16

U32

MLX

Hardware compatibility

Quantized

Model tree for finansai/BIST-Financial-Qwen-7B

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(312)

this model

Datasets used to train finansai/BIST-Financial-Qwen-7B

Paper for finansai/BIST-Financial-Qwen-7B

Qwen Technical Report

Paper • 2309.16609 • Published Sep 28, 2023 • 39