Instructions to use slymntgyn/Llama3.1-IFS9-Text-SQL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use slymntgyn/Llama3.1-IFS9-Text-SQL with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="slymntgyn/Llama3.1-IFS9-Text-SQL",
	filename="llama-3.1-8b.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use slymntgyn/Llama3.1-IFS9-Text-SQL with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf slymntgyn/Llama3.1-IFS9-Text-SQL:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf slymntgyn/Llama3.1-IFS9-Text-SQL:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf slymntgyn/Llama3.1-IFS9-Text-SQL:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf slymntgyn/Llama3.1-IFS9-Text-SQL: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 slymntgyn/Llama3.1-IFS9-Text-SQL:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf slymntgyn/Llama3.1-IFS9-Text-SQL: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 slymntgyn/Llama3.1-IFS9-Text-SQL:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf slymntgyn/Llama3.1-IFS9-Text-SQL:Q4_K_M

Use Docker

docker model run hf.co/slymntgyn/Llama3.1-IFS9-Text-SQL:Q4_K_M

LM Studio
Jan
Ollama
How to use slymntgyn/Llama3.1-IFS9-Text-SQL with Ollama:
```
ollama run hf.co/slymntgyn/Llama3.1-IFS9-Text-SQL:Q4_K_M
```

Unsloth Studio new

How to use slymntgyn/Llama3.1-IFS9-Text-SQL 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 slymntgyn/Llama3.1-IFS9-Text-SQL 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 slymntgyn/Llama3.1-IFS9-Text-SQL to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for slymntgyn/Llama3.1-IFS9-Text-SQL to start chatting

Docker Model Runner
How to use slymntgyn/Llama3.1-IFS9-Text-SQL with Docker Model Runner:
```
docker model run hf.co/slymntgyn/Llama3.1-IFS9-Text-SQL:Q4_K_M
```

Lemonade

How to use slymntgyn/Llama3.1-IFS9-Text-SQL with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull slymntgyn/Llama3.1-IFS9-Text-SQL:Q4_K_M

Run and chat with the model

lemonade run user.Llama3.1-IFS9-Text-SQL-Q4_K_M

List all available models

lemonade list

Llama3.1-IFS9-Text-SQL

File size: 968 Bytes

e67c31d

{
    "architectures": [
        "LlamaForCausalLM"
    ],
    "attention_bias": false,
    "attention_dropout": 0.0,
    "bos_token_id": 128000,
    "torch_dtype": "float16",
    "eos_token_id": 128001,
    "head_dim": 128,
    "hidden_act": "silu",
    "hidden_size": 4096,
    "initializer_range": 0.02,
    "intermediate_size": 14336,
    "max_position_embeddings": 131072,
    "mlp_bias": false,
    "model_type": "llama",
    "num_attention_heads": 32,
    "num_hidden_layers": 32,
    "num_key_value_heads": 8,
    "pad_token_id": 128004,
    "pretraining_tp": 1,
    "rms_norm_eps": 1e-05,
    "rope_scaling": {
        "factor": 8.0,
        "high_freq_factor": 4.0,
        "low_freq_factor": 1.0,
        "original_max_position_embeddings": 8192,
        "rope_type": "llama3"
    },
    "rope_theta": 500000.0,
    "tie_word_embeddings": false,
    "unsloth_fixed": true,
    "unsloth_version": "2026.4.4",
    "use_cache": true,
    "vocab_size": 128256
}