Instructions to use Italianhype/Blum-Finance-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Italianhype/Blum-Finance-4B with Transformers:

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

pipe = pipeline("text-generation", model="Italianhype/Blum-Finance-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Italianhype/Blum-Finance-4B")
model = AutoModelForCausalLM.from_pretrained("Italianhype/Blum-Finance-4B", device_map="auto")
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 Italianhype/Blum-Finance-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Italianhype/Blum-Finance-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Italianhype/Blum-Finance-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Italianhype/Blum-Finance-4B

SGLang

How to use Italianhype/Blum-Finance-4B 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 "Italianhype/Blum-Finance-4B" \
    --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": "Italianhype/Blum-Finance-4B",
		"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 "Italianhype/Blum-Finance-4B" \
        --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": "Italianhype/Blum-Finance-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Italianhype/Blum-Finance-4B with Docker Model Runner:
```
docker model run hf.co/Italianhype/Blum-Finance-4B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BLUM Finance 4B — Transformers Benchmark Release

BLUM Finance 4B is an open, evidence-bound financial reasoning model. This repository is the portable BF16 Transformers release used for public benchmark submission. It contains the same trained LoRA delta as the audited MLX release, mapped and fused into the exact Qwen/Qwen3-4B base architecture.

Intended Use

The model structures supplied point-in-time evidence into:

balanced bull and bear cases;
explicit risks and invalidation conditions;
conservative confidence;
monitoring conditions and abstention.

It is not a price oracle, broker, trade executor or proof of market alpha.

Load

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Italianhype/Blum-Finance-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
)

No trust_remote_code=True is required.

Lineage

Base: Qwen/Qwen3-4B
Base revision: 1cfa9a7208912126459214e8b04321603b3df60c
Dataset: Italianhype/Blum-Finance-Reasoning
Dataset revision: 76ad77699d498fc930daf02e452fe3ec8b490f90
MLX adapter revision: ea297ba88ab008e97104b0c118103eef2f8f9ec1
LoRA rank: 8
MLX scale: 20
Equivalent PEFT alpha: 160
Adapted layers: 20–35

The conversion transposes MLX A/B matrices into PEFT orientation and preserves the update 20 × Bᵀ × Aᵀ. All 112 expected modules were fused; none were missing. The PEFT adapter and merged model produced identical deterministic smoke-test output.

Evaluation Status

The original MLX release passed the 53-example BLUM temporal reasoning test with 96.26% aggregate task-contract score, 100% structured validity and 92.86% no-fabrication. These metrics measure BLUM schema adherence and grounding, not general intelligence or trading performance.

The portable release is published at immutable revision ad6f5cec7f729370d2976d8c78983521cb37ca83 and tagged benchmark-submission-v1.

The five-example portable conversion smoke test scored 97.32% on the internal task contract, including 100% structured validity and 92.86% no-fabrication. The sample is too small for a robust capability claim.

Automated submissions were attempted for the Hugging Face Open LLM Leaderboard and the FinOS Open Financial LLM Leaderboard. Both external validators rejected Qwen3 as requiring trust_remote_code=True; Transformers 4.57.6 loads this repository without remote code. No official leaderboard score is claimed.

A deterministic stratified community evaluation on seven finance/business subjects of canonical cais/mmlu scored 74.86% accuracy over 700 test questions (100 per subject), with a Wilson 95% confidence interval of 71.51–77.93%. It used five-shot answer-token logit scoring.

Subject	Accuracy	Samples
Business ethics	77%	100
Econometrics	65%	100
High-school macroeconomics	70%	100
High-school microeconomics	87%	100
Management	87%	100
Marketing	92%	100
Professional accounting	46%	100

This is an author-run community evaluation, not the full canonical MMLU suite and not an official leaderboard result. The deterministic 100-row cap gives each subject equal weight but can differ from a full-split result. Dataset revision: c30699e8356da336a370243923dbaf21066bb9fe.

Evaluation summary · Per-example predictions · Evaluator

Independent evaluation packages are prepared for Vals AI CorpFin/Finance Agent and Scale Labs PRBench Finance. Those scores remain unavailable until the benchmark owners evaluate the immutable model revision.

Known Failure

A sparse-evidence smoke prompt incorrectly identified NVDA as Applied Materials. This is a documented fabrication failure. Callers must validate company identity and all market facts against BLUM Engine or another authoritative data source.