Instructions to use GTO83/modelos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GTO83/modelos with Transformers:

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

pipe = pipeline("text-generation", model="GTO83/modelos")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("GTO83/modelos", device_map="auto")

llama-cpp-python

How to use GTO83/modelos with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="GTO83/modelos",
	filename="Qwen_Qwen2.5-Coder-7B-Instruct-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use GTO83/modelos 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 GTO83/modelos:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GTO83/modelos:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf GTO83/modelos:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GTO83/modelos: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 GTO83/modelos:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf GTO83/modelos: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 GTO83/modelos:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GTO83/modelos:Q4_K_M

Use Docker

docker model run hf.co/GTO83/modelos:Q4_K_M

LM Studio
Jan

vLLM

How to use GTO83/modelos with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GTO83/modelos"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GTO83/modelos",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GTO83/modelos:Q4_K_M

SGLang

How to use GTO83/modelos 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 "GTO83/modelos" \
    --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": "GTO83/modelos",
		"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 "GTO83/modelos" \
        --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": "GTO83/modelos",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use GTO83/modelos with Ollama:
```
ollama run hf.co/GTO83/modelos:Q4_K_M
```

Unsloth Studio

How to use GTO83/modelos 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 GTO83/modelos 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 GTO83/modelos to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for GTO83/modelos to start chatting

How to use GTO83/modelos with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GTO83/modelos:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "GTO83/modelos:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use GTO83/modelos with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GTO83/modelos:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default GTO83/modelos:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use GTO83/modelos with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GTO83/modelos:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "GTO83/modelos:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use GTO83/modelos with Docker Model Runner:
```
docker model run hf.co/GTO83/modelos:Q4_K_M
```

Lemonade

How to use GTO83/modelos with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GTO83/modelos:Q4_K_M

Run and chat with the model

lemonade run user.modelos-Q4_K_M

List all available models

lemonade list

GTO83 commited on Feb 1

Commit

5ad9019

verified ·

1 Parent(s): 2ff06a0

Create README.md

Browse files

Files changed (1) hide show

README.md +60 -0

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	@@ -0,0 +1,60 @@

+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct/blob/main/LICENSE
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Coder-1.5B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- code
+- codeqwen
+- chat
+- qwen
+- qwen-coder
+---
+# Qwen2.5-Coder-1.5B-Instruct-GGUF
+<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
+</a>
+<hr>
+# Perplexity table (the lower the better)
+| Quant   | Size (MB) | PPL      | Size (%) | Accuracy (%) | PPL error rate |
+| ------- | --------- | -------- | -------- | ------------ | -------------- |
+| IQ1_S   | 417       | 193.6245 | 14.13    | 5.24         | 1.77149        |
+| IQ1_M   | 443       | 66.9068  | 15.01    | 15.17        | 0.52878        |
+| IQ2_XXS | 488       | 33.3356  | 16.54    | 30.45        | 0.25559        |
+| IQ2_XS  | 525       | 20.287   | 17.79    | 50.04        | 0.14936        |
+| IQ2_S   | 538       | 18.2927  | 18.23    | 55.49        | 0.1338         |
+| IQ2_M   | 574       | 15.4838  | 19.45    | 65.56        | 0.11113        |
+| Q2_K_S  | 611       | 16.0169  | 20.7     | 63.38        | 0.11623        |
+| IQ3_XXS | 638       | 12.3935  | 21.62    | 81.91        | 0.0877         |
+| Q2_K    | 645       | 14.1657  | 21.86    | 71.66        | 0.10105        |
+| IQ3_XS  | 698       | 11.7112  | 23.65    | 86.68        | 0.08256        |
+| Q3_K_S  | 726       | 12.4782  | 24.6     | 81.35        | 0.08842        |
+| IQ3_S   | 728       | 11.4241  | 24.67    | 88.86        | 0.07977        |
+| IQ3_M   | 741       | 11.4058  | 25.11    | 89           | 0.07862        |
+| Q3_K_M  | 786       | 11.3529  | 26.64    | 89.42        | 0.08018        |
+| Q3_K_L  | 840       | 11.1934  | 28.46    | 90.69        | 0.07913        |
+| IQ4_XS  | 855       | 10.5302  | 28.97    | 96.4         | 0.07351        |
+| IQ4_NL  | 893       | 10.5116  | 30.26    | 96.57        | 0.07335        |
+| Q4_0    | 895       | 10.8217  | 30.33    | 93.8         | 0.07576        |
+| Q4_K_S  | 897       | 10.5236  | 30.4     | 96.46        | 0.0736         |
+| Q4_K_M  | 941       | 10.4628  | 31.89    | 97.02        | 0.0731         |
+| Q4_1    | 970       | 10.51    | 32.87    | 96.59        | 0.07347        |
+| Q5_K_S  | 1048      | 10.2715  | 35.51    | 98.83        | 0.07148        |
+| Q5_0    | 1051      | 10.3196  | 35.62    | 98.37        | 0.07212        |
+| Q5_K_M  | 1073      | 10.2529  | 36.36    | 99.01        | 0.07143        |
+| Q5_1    | 1126      | 10.2624  | 38.16    | 98.92        | 0.0714         |
+| Q6_K    | 1214      | 10.203   | 41.14    | 99.49        | 0.07108        |
+| Q8_0    | 1571      | 10.167   | 53.24    | 99.84        | 0.07068        |
+| F16     | 2951      | 10.1512  | 100      | 100          | 0.07058        |
+<hr>