Instructions to use QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/sepctrum-ties-sqlcoder-8b-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 QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF: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 QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF: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 QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/sepctrum-ties-sqlcoder-8b-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 QuantFactory/sepctrum-ties-sqlcoder-8b-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 QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.sepctrum-ties-sqlcoder-8b-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

sepctrum-ties-sqlcoder-8b-GGUF / README.md

aashish1904

Upload README.md with huggingface_hub

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	---

	base_model:
	- defog/llama-3-sqlcoder-8b
	- meta-llama/Meta-Llama-3-8B-Instruct
	library_name: transformers
	tags:
	- mergekit
	- merge


	---

	![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)

	# QuantFactory/sepctrum-ties-sqlcoder-8b-GGUF
	This is quantized version of [arcee-ai/sepctrum-ties-sqlcoder-8b](https://huggingface.co/arcee-ai/sepctrum-ties-sqlcoder-8b) created using llama.cpp

	# Original Model Card

	# merge

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base.

	### Models Merged

	The following models were included in the merge:
	* [defog/llama-3-sqlcoder-8b](https://huggingface.co/defog/llama-3-sqlcoder-8b)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	merge_method: ties
	base_model: meta-llama/Meta-Llama-3-8B-Instruct
	models:
	- model: defog/llama-3-sqlcoder-8b
	parameters:
	weight:
	- filter: mlp.down_proj
	value: [0, 0, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0.5, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0]
	- filter: mlp.gate_proj
	value: [0, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.5]
	- filter: mlp.up_proj
	value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.5, 0, 0, 0, 0, 0.5, 0, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
	- filter: self_attn.k_proj
	value: [0.5, 0.5, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0]
	- filter: self_attn.o_proj
	value: [0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0.5, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0]
	- filter: self_attn.q_proj
	value: [0, 0, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5]
	- filter: self_attn.v_proj
	value: [0.5, 0, 0.5, 0, 0, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0.5, 0.5, 0, 0, 0, 0, 0.5, 0, 0, 0.5, 0, 0, 0.5, 0.5]
	- value: [0]
	density: 0.75
	- model: meta-llama/Meta-Llama-3-8B-Instruct
	parameters:
	weight:
	- filter: mlp.down_proj
	value: [1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 0.5, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1]
	- filter: mlp.gate_proj
	value: [1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5]
	- filter: mlp.up_proj
	value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 1, 1, 0.5, 1, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
	- filter: self_attn.k_proj
	value: [0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 1]
	- filter: self_attn.o_proj
	value: [0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 0.5, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 1]
	- filter: self_attn.q_proj
	value: [1, 1, 0.5, 0.5, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5]
	- filter: self_attn.v_proj
	value: [0.5, 1, 0.5, 1, 1, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 1, 1, 0.5, 1, 1, 0.5, 1, 1, 0.5, 0.5]
	- value: [1]
	density: 1.0
	parameters: {normalize: true, int8_mask: true}
	dtype: bfloat16

	```