Instructions to use zero-systems/StructuredCoder-7b.GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zero-systems/StructuredCoder-7b.GGUF with Transformers:

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

pipe = pipeline("text-generation", model="zero-systems/StructuredCoder-7b.GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zero-systems/StructuredCoder-7b.GGUF")
model = AutoModelForCausalLM.from_pretrained("zero-systems/StructuredCoder-7b.GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use zero-systems/StructuredCoder-7b.GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf zero-systems/StructuredCoder-7b.GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf zero-systems/StructuredCoder-7b.GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf zero-systems/StructuredCoder-7b.GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf zero-systems/StructuredCoder-7b.GGUF:Q4_0

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 zero-systems/StructuredCoder-7b.GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf zero-systems/StructuredCoder-7b.GGUF:Q4_0

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 zero-systems/StructuredCoder-7b.GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zero-systems/StructuredCoder-7b.GGUF:Q4_0

Use Docker

docker model run hf.co/zero-systems/StructuredCoder-7b.GGUF:Q4_0

LM Studio
Jan

vLLM

How to use zero-systems/StructuredCoder-7b.GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/zero-systems/StructuredCoder-7b.GGUF:Q4_0

SGLang

How to use zero-systems/StructuredCoder-7b.GGUF 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 "zero-systems/StructuredCoder-7b.GGUF" \
    --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": "zero-systems/StructuredCoder-7b.GGUF",
		"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 "zero-systems/StructuredCoder-7b.GGUF" \
        --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": "zero-systems/StructuredCoder-7b.GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use zero-systems/StructuredCoder-7b.GGUF with Ollama:
```
ollama run hf.co/zero-systems/StructuredCoder-7b.GGUF:Q4_0
```

Unsloth Studio

How to use zero-systems/StructuredCoder-7b.GGUF 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 zero-systems/StructuredCoder-7b.GGUF 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 zero-systems/StructuredCoder-7b.GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for zero-systems/StructuredCoder-7b.GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use zero-systems/StructuredCoder-7b.GGUF with Docker Model Runner:
```
docker model run hf.co/zero-systems/StructuredCoder-7b.GGUF:Q4_0
```

Lemonade

How to use zero-systems/StructuredCoder-7b.GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zero-systems/StructuredCoder-7b.GGUF:Q4_0

Run and chat with the model

lemonade run user.StructuredCoder-7b.GGUF-Q4_0

List all available models

lemonade list

StructuredCoder-7b.GGUF / README.md

minosu

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	---
	datasets:
	- zero-systems/StringConversion.7k.INSTRUCT_DPO
	---

	# StructuredCoder-7b.GGUF

	<!-- Provide a quick summary of what the model is/does. -->
	StructuredCoder models aim to identify formats of data represented within sets of strings, and convert strings of set A to the format of set B via generated Python code.

	## Model Details

	### Model Description

	- Model type: LLM
	- Finetuned from model [optional]: [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct)

	## Inference

	`.gguf` models can be inferenced using [llama.cpp](https://github.com/ggerganov/llama.cpp) ([llama-cpp-python](https://github.com/abetlen/llama-cpp-python)).
	Please follow the instructions within these repos to get started.

	### Inference Examples

	<!-- Example prompts or input data that the model is expected to handle, and examples of expected output -->

	This is an instruct finetune utilizing the Alpaca instruct format (introduced by [stanford-alpaca](https://github.com/tatsu-lab/stanford_alpaca)):

	```python
	"{system_prompt}\n\n### Instruction:\n{instruction}\n\n### Response: "
	```

	The model is finetuned to perform the following task:

	#### Structured Data Conversion

	Expected input:

	```
	You are a coding assistant that specializes in converting string values from an input to a target format.
	You will be given a set of input sets, each in itself a set of example string values plus a corresponding title, as well as a set of example target strings plus a corresponding title.
	Your task is to analyze the string formats and identify whether or not the input sets can be converted into the target set using a single Python method, and then to write that method.

	You will also be given a short piece of context that may be helpful to arrive at your decision.

	### Instruction:
	Input Sets:

	Title: OldPlanServiceRate
	Example Values:
	[
	"$50.15"
	"$70.10"
	"$90.30"
	"$44.20"
	"$50.10"
	"$90.36"
	]


	Title: OldPlanQuantity
	Example Values:
	[
	"5"
	"5"
	"2"
	"4"
	"2"
	"8"
	]


	Target Set:

	Title: OldPlanServiceAmount
	Example Values:
	[
	"$75.30"
	"$325.90"
	"$175.80"
	"$150.75"
	"$525.50"
	"$400.50"
	]

	Context:
	1. The target key suggests a monetary amount related to an 'Old Plan' service. 2. The values of the target data are monetary amounts formatted as currency. 3. 'OldPlanServiceRate' provides a monetary rate for a service and 'OldPlanQuantity' specifies a quantity, which when multiplied together, would give a monetary amount similar to the values present in 'OldPlanServiceAmount'.

	### Response:
	```

	Expected Output:

	```
	{
	"reasoning": "Remove the dollar sign from OldPlanServiceRate, convert it to a float and OldPlanQuantity to an integer. Then multiply the two together to get the service amount. The result should be formatted as a currency string with two decimal places and a dollar sign.",
	"test_input_values": {
	"OldPlanServiceRate": "$50.25",
	"OldPlanQuantity": "10"
	},
	"test_expected_output_value": "$502.50",
	"conversion_code": "def convert(old_plan_service_rate: str, old_plan_quantity: str) -> str:\n import re\n service_rate = float(re.sub('[$]', '', old_plan_service_rate))\n quantity = int(old_plan_quantity)\n return '${:.2f}'.format(service_rate * quantity)"
	}
	```

	### Training Data

	<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
	[zero-systems/StringConversion.7k.INSTRUCT_DPO](https://huggingface.co/datasets/zero-systems/StringConversion.7k.INSTRUCT_DPO)

	#### Training Methodology

	<!--Summary of methodology used to train model -->
	StructuredLLM was trained using [QLoRA](https://github.com/artidoro/qlora).
	Resulting adapter was merged into the base model weights, converted to the `gguf` format and finally quantized to 4 bits.