Instructions to use ccortezb/FinoCode-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ccortezb/FinoCode-7B with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ccortezb/FinoCode-7B")
model = AutoModelForCausalLM.from_pretrained("ccortezb/FinoCode-7B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ccortezb/FinoCode-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ccortezb/FinoCode-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": "ccortezb/FinoCode-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ccortezb/FinoCode-7B

SGLang

How to use ccortezb/FinoCode-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 "ccortezb/FinoCode-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": "ccortezb/FinoCode-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 "ccortezb/FinoCode-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": "ccortezb/FinoCode-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use ccortezb/FinoCode-7B with Docker Model Runner:
```
docker model run hf.co/ccortezb/FinoCode-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

FinoCode-7B — The IaC Specialist of the FinOptix family

FinoCode-7B is the infrastructure-as-code specialist of the FinOptix model family. Fine-tuned on Qwen 2.5 Coder 7B, it audits and generates Terraform, AWS CDK (TypeScript & Python), and CloudFormation with FinOps best practices baked in — rightsizing, mandatory tags, Graviton, lifecycle policies, encryption.

Where FinOptix-14B is the full FinOps architect (cost analysis, architecture graph, dbt, incident triage), FinoCode-7B writes and fixes the actual IaC code — it's built on a Coder base precisely for that.

16-bit model: ccortezb/FinoCode-7B (this repo)
GGUF (Ollama / llama.cpp): ccortezb/FinoCode-7B-GGUF — Q4_K_M (~~4.7GB) and Q6_K (~~6.3GB)

What it does

Capability	Detail
Terraform	Audit + refactor + generation (rightsizing, Graviton, gp2→gp3, S3 lifecycle, RDS, tags)
AWS CDK (TypeScript)	Audit constructs + generate cost-optimal ones (ARM64, log retention, PAY_PER_REQUEST)
AWS CDK (Python)	Same, Python-native (EC2 Graviton, S3 lifecycle, tags)
CloudFormation	YAML audit + corrected templates (EC2, S3, Lambda, RDS, ECS Fargate, SQS DLQ)

Every recommendation follows the FinOps framework (Inform → Optimize → Operate) and outputs valid, properly-indented code.

Training

Parameter	Value
Base model	`Qwen/Qwen2.5-Coder-7B-Instruct` (4-bit via Unsloth)
Method	QLoRA (r=16, α=32, dropout=0.05)
Framework	Unsloth + TRL (SFTTrainer)
Dataset	400 examples IaC-heavy (200 Terraform + 100 CloudFormation + 60 CDK TS + 40 CDK Python)
Hardware	Modal A100-80GB
Duration	~3 minutes, 3 epochs, 75 steps
Final train loss	0.47
Cost	~$0.30

The dataset was built with deterministic generators faithful to real-world IaC patterns — 100% of examples contain valid code blocks.

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ccortezb/FinoCode-7B", device_map="auto")
tok = AutoTokenizer.from_pretrained("ccortezb/FinoCode-7B")

messages = [
    {"role": "system", "content": "You are FinoCode-7B, the IaC specialist of the FinOptix family. Output valid code."},
    {"role": "user", "content": "Generate a cost-optimal AWS CDK (TypeScript) DynamoDB table with on-demand billing and encryption."},
]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=350, do_sample=False, repetition_penalty=1.1)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

Ollama (GGUF)

ollama run hf.co/ccortezb/FinoCode-7B-GGUF:Q4_K_M
# higher fidelity:
ollama run hf.co/ccortezb/FinoCode-7B-GGUF:Q6_K

Requires ~5GB RAM/VRAM (Q4_K_M) or ~7GB (Q6_K) — runs comfortably on a laptop.

Example tasks

CDK Python audit → flags a m5.4xlarge with no tags, returns corrected Python with Graviton (m6g) + Tags.of(...).

CloudFormation audit → flags missing tags + x86 instance, returns corrected YAML with Graviton (m6g.large) and a Tags block.

Generation → produces cost-optimal constructs: DynamoDB PAY_PER_REQUEST, S3 lifecycle (IA → Glacier → expire), Lambda on ARM64 with 2-week log retention, SQS with a dead-letter queue.

The FinOptix family

Model	Base	Specialty
FinOptix-14B v2	Qwen 2.5 14B	Full FinOps architect (cost, graph, dbt, incidents)
FinoCode-7B (this)	Qwen 2.5 Coder 7B	IaC audit + generation

Use case: IDE copilot for IaC, PR review bot, CI pipeline IaC optimizer.

Limitations

Trained on synthetic data modeled after real patterns — no customer data.
Generates and audits IaC; does not deploy or call live AWS APIs.
Cost figures are illustrative unless real metrics are provided.
v0.1 — Terraform + CDK (TS/Py) + CloudFormation. Pulumi / Terraform CDK / multi-cloud are future work.

License

Apache-2.0. Built by Carlos Cortez — AWS Community Hero. LinkedIn · X · GitHub

Downloads last month: 129

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for ccortezb/FinoCode-7B

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Finetuned

(491)

this model

Quantizations

1 model

ccortezb
/

FinoCode-7B

FinoCode-7B — The IaC Specialist of the FinOptix family

What it does

Training

Usage

Transformers

Ollama (GGUF)

Example tasks

The FinOptix family

Limitations

License

Model tree for ccortezb/FinoCode-7B

Space using ccortezb/FinoCode-7B 1