Instructions to use cpt-eug/text2sql-llama3sqlcoder-cdm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cpt-eug/text2sql-llama3sqlcoder-cdm with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cpt-eug/text2sql-llama3sqlcoder-cdm", dtype="auto") - llama-cpp-python
How to use cpt-eug/text2sql-llama3sqlcoder-cdm with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="cpt-eug/text2sql-llama3sqlcoder-cdm", filename="text2sql-llama3sqlcoder-cdm-unsloth.Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use cpt-eug/text2sql-llama3sqlcoder-cdm with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cpt-eug/text2sql-llama3sqlcoder-cdm:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cpt-eug/text2sql-llama3sqlcoder-cdm:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cpt-eug/text2sql-llama3sqlcoder-cdm:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cpt-eug/text2sql-llama3sqlcoder-cdm: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 cpt-eug/text2sql-llama3sqlcoder-cdm:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cpt-eug/text2sql-llama3sqlcoder-cdm: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 cpt-eug/text2sql-llama3sqlcoder-cdm:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cpt-eug/text2sql-llama3sqlcoder-cdm:Q4_K_M
Use Docker
docker model run hf.co/cpt-eug/text2sql-llama3sqlcoder-cdm:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use cpt-eug/text2sql-llama3sqlcoder-cdm with Ollama:
ollama run hf.co/cpt-eug/text2sql-llama3sqlcoder-cdm:Q4_K_M
- Unsloth Studio new
How to use cpt-eug/text2sql-llama3sqlcoder-cdm 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 cpt-eug/text2sql-llama3sqlcoder-cdm 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 cpt-eug/text2sql-llama3sqlcoder-cdm to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cpt-eug/text2sql-llama3sqlcoder-cdm to start chatting
- Docker Model Runner
How to use cpt-eug/text2sql-llama3sqlcoder-cdm with Docker Model Runner:
docker model run hf.co/cpt-eug/text2sql-llama3sqlcoder-cdm:Q4_K_M
- Lemonade
How to use cpt-eug/text2sql-llama3sqlcoder-cdm with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cpt-eug/text2sql-llama3sqlcoder-cdm:Q4_K_M
Run and chat with the model
lemonade run user.text2sql-llama3sqlcoder-cdm-Q4_K_M
List all available models
lemonade list
File size: 730 Bytes
b35d7bd 0585139 b35d7bd 0585139 b35d7bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | {
"alpha_pattern": {},
"auto_mapping": null,
"base_model_name_or_path": "defog/llama-3-sqlcoder-8b",
"bias": "none",
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"layer_replication": null,
"layers_pattern": null,
"layers_to_transform": null,
"loftq_config": {},
"lora_alpha": 32,
"lora_dropout": 0,
"megatron_config": null,
"megatron_core": "megatron.core",
"modules_to_save": null,
"peft_type": "LORA",
"r": 32,
"rank_pattern": {},
"revision": "unsloth",
"target_modules": [
"k_proj",
"o_proj",
"v_proj",
"down_proj",
"gate_proj",
"up_proj",
"q_proj"
],
"task_type": "CAUSAL_LM",
"use_dora": false,
"use_rslora": false
} |