Instructions to use Safeeq/pycoder-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Safeeq/pycoder-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 Safeeq/pycoder-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf Safeeq/pycoder-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Safeeq/pycoder-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf Safeeq/pycoder-gguf:Q8_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 Safeeq/pycoder-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Safeeq/pycoder-gguf:Q8_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 Safeeq/pycoder-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Safeeq/pycoder-gguf:Q8_0

Use Docker

docker model run hf.co/Safeeq/pycoder-gguf:Q8_0

LM Studio
Jan
Ollama
How to use Safeeq/pycoder-gguf with Ollama:
```
ollama run hf.co/Safeeq/pycoder-gguf:Q8_0
```
Unsloth Desktop

How to use Safeeq/pycoder-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Safeeq/pycoder-gguf:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Safeeq/pycoder-gguf:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Safeeq/pycoder-gguf with Docker Model Runner:
```
docker model run hf.co/Safeeq/pycoder-gguf:Q8_0
```

Lemonade

How to use Safeeq/pycoder-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Safeeq/pycoder-gguf:Q8_0

Run and chat with the model

lemonade run user.pycoder-gguf-Q8_0

List all available models

lemonade list

Hermes Agent

How to use Safeeq/pycoder-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Safeeq/pycoder-gguf:Q8_0

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 Safeeq/pycoder-gguf:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Safeeq/pycoder-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Safeeq/pycoder-gguf:Q8_0

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 "Safeeq/pycoder-gguf:Q8_0" \
  --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"

pycoder-gguf / train.py

Safeeq

Publish complete PyCoder model and usage files

52c8e55 verified 6 days ago

Raw History Blame Contribute Delete

886 Bytes

	from datasets import load_dataset
	from peft import LoraConfig
	from trl import SFTTrainer, SFTConfig
	from common import BASE

	ds = load_dataset("json", data_files="data.jsonl")["train"]

	trainer = SFTTrainer(
	model=BASE,
	train_dataset=ds,
	peft_config=LoraConfig(
	r=32, lora_alpha=64, lora_dropout=0.05,
	target_modules="all-linear", task_type="CAUSAL_LM",
	),
	args=SFTConfig(
	output_dir="out",
	num_train_epochs=3,
	per_device_train_batch_size=2,
	gradient_accumulation_steps=4,
	learning_rate=2e-4,
	bf16=False,
	max_length=1024,
	logging_steps=10,
	save_strategy="no",
	dataloader_num_workers=0, # avoids Windows DataLoader errors
	report_to="none",
	),
	)
	trainer.train()
	trainer.save_model("lora-out")
	print("Adapter saved to lora-out/")