Instructions to use heommi/fintech_2026 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use heommi/fintech_2026 with Transformers:

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

pipe = pipeline("text-generation", model="heommi/fintech_2026")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("heommi/fintech_2026")
model = AutoModelForCausalLM.from_pretrained("heommi/fintech_2026")
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]:]))

llama-cpp-python

How to use heommi/fintech_2026 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="heommi/fintech_2026",
	filename="kopo_2026_gemma4b_heomingi.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use heommi/fintech_2026 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf heommi/fintech_2026
# Run inference directly in the terminal:
llama-cli -hf heommi/fintech_2026

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf heommi/fintech_2026
# Run inference directly in the terminal:
llama-cli -hf heommi/fintech_2026

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 heommi/fintech_2026
# Run inference directly in the terminal:
./llama-cli -hf heommi/fintech_2026

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 heommi/fintech_2026
# Run inference directly in the terminal:
./build/bin/llama-cli -hf heommi/fintech_2026

Use Docker

docker model run hf.co/heommi/fintech_2026

LM Studio
Jan

vLLM

How to use heommi/fintech_2026 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "heommi/fintech_2026"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "heommi/fintech_2026",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/heommi/fintech_2026

SGLang

How to use heommi/fintech_2026 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 "heommi/fintech_2026" \
    --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": "heommi/fintech_2026",
		"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 "heommi/fintech_2026" \
        --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": "heommi/fintech_2026",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use heommi/fintech_2026 with Ollama:
```
ollama run hf.co/heommi/fintech_2026
```

Unsloth Studio

How to use heommi/fintech_2026 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 heommi/fintech_2026 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 heommi/fintech_2026 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for heommi/fintech_2026 to start chatting

Docker Model Runner
How to use heommi/fintech_2026 with Docker Model Runner:
```
docker model run hf.co/heommi/fintech_2026
```

Lemonade

How to use heommi/fintech_2026 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull heommi/fintech_2026

Run and chat with the model

lemonade run user.fintech_2026-{{QUANT_TAG}}

List all available models

lemonade list

heommi commited on Apr 10

Commit

78e10da

verified ·

1 Parent(s): 4bbfde0

Upload Gemma3ForConditionalGeneration

Browse files

Files changed (5) hide show

config.json +54 -29
generation_config.json +0 -2
model-00001-of-00002.safetensors +2 -2
model-00002-of-00002.safetensors +2 -2
model.safetensors.index.json +0 -0

config.json CHANGED Viewed

@@ -1,37 +1,62 @@
 {
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-  "use_cache": true,
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 }

 {
   "architectures": [
+    "Gemma3ForConditionalGeneration"
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+    "cache_implementation": "hybrid",
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+    "model_type": "gemma3_text",
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+    "num_hidden_layers": 34,
+    "num_key_value_heads": 4,
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+    "rms_norm_eps": 1e-06,
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+    "rope_scaling": {
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+    "sliding_window": 1024,
+    "sliding_window_pattern": 6,
+    "torch_dtype": "bfloat16",
+    "use_cache": true,
+    "vocab_size": 262208
   },
   "torch_dtype": "bfloat16",
   "transformers_version": "4.51.3",
+  "vision_config": {
+    "attention_dropout": 0.0,
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "image_size": 896,
+    "intermediate_size": 4304,
+    "layer_norm_eps": 1e-06,
+    "model_type": "siglip_vision_model",
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+    "num_hidden_layers": 27,
+    "patch_size": 14,
+    "torch_dtype": "bfloat16",
+    "vision_use_head": false
+  }
 }

generation_config.json CHANGED Viewed

@@ -1,5 +1,4 @@
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   "do_sample": true,
@@ -8,7 +7,6 @@
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