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

Libraries

How to use devaloper/codeas with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="devaloper/codeas",
	filename="codeas-model-Q6_K.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 devaloper/codeas 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 devaloper/codeas:Q6_K
# Run inference directly in the terminal:
llama cli -hf devaloper/codeas:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf devaloper/codeas:Q6_K
# Run inference directly in the terminal:
llama cli -hf devaloper/codeas:Q6_K

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 devaloper/codeas:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf devaloper/codeas:Q6_K

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 devaloper/codeas:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf devaloper/codeas:Q6_K

Use Docker

docker model run hf.co/devaloper/codeas:Q6_K

LM Studio
Jan

vLLM

How to use devaloper/codeas with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/devaloper/codeas:Q6_K

Ollama
How to use devaloper/codeas with Ollama:
```
ollama run hf.co/devaloper/codeas:Q6_K
```

Unsloth Studio

How to use devaloper/codeas 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 devaloper/codeas 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 devaloper/codeas to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use devaloper/codeas with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devaloper/codeas:Q6_K

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": "devaloper/codeas:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use devaloper/codeas with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devaloper/codeas:Q6_K

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 devaloper/codeas:Q6_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use devaloper/codeas with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devaloper/codeas:Q6_K

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 "devaloper/codeas:Q6_K" \
  --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 devaloper/codeas with Docker Model Runner:
```
docker model run hf.co/devaloper/codeas:Q6_K
```

Lemonade

How to use devaloper/codeas with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull devaloper/codeas:Q6_K

Run and chat with the model

lemonade run user.codeas-Q6_K

List all available models

lemonade list

codeas / README.md

devaloper

Update README.md

64a3d35 verified 4 months ago

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metadata

license: apache-2.0
library_name: gguf
base_model: Qwen/Qwen3-14B
tags:
  - code
  - qwen3
  - gguf
  - fine-tuned
  - conversational
model-index:
  - name: Codeas Model
    results: []
pipeline_tag: text-generation
language:
  - en

Codeas Model

A fine-tuned Qwen3-14B model optimized for code generation and reasoning tasks. Available in GGUF Q6_K format for efficient local inference.

Model Details


Base Model	Qwen3-14B
Parameters	~15B
Architecture	Qwen3 (GQA, RoPE)
Context Length	40,960 tokens
Precision	BF16 (original), Q6_K (GGUF)
License	Apache 2.0

Architecture

40 transformer blocks
40 attention heads, 8 KV heads (Grouped Query Attention)
5,120 hidden size / 17,408 FFN size
RoPE with 1M frequency base
SiLU activation
151,936 vocab size (GPT-2 tokenizer, Qwen2 pre-tokenizer)

Capabilities

Chain-of-thought reasoning via <think> blocks
Tool/function calling via <tool_call> format
Thinking mode can be toggled on/off per request

GGUF Quantizations

File	Quant	Size	Quality
`codeas-model-Q6_K.gguf`	Q6_K	12.1 GB	Near-lossless

Usage

llama.cpp

./llama-cli -m codeas-model-Q6_K.gguf -p "Write a Python function to merge two sorted lists" -n 512

Ollama

Create a Modelfile with the following content:

FROM ./codeas-model-Q6_K.gguf

PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER top_k 20

TEMPLATE """{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""

SYSTEM "You are Codeas, a helpful coding assistant."

Then run:

ollama create codeas -f Modelfile
ollama run codeas

Hardware Requirements

Format	VRAM / RAM
Q6_K GGUF	~14 GB

Training


Method	Full fine-tune (no LoRA)
Framework	Axolotl 0.13.0 + Transformers 4.55.4
Hardware	8x GPU (FSDP)
Optimizer	AdamW (fused)
LR Schedule	Cosine, 1e-5 peak
Sequence Length	8,192
Batch Size	24 (3 per device)
Epochs	3
Precision	BF16 + TF32
Techniques	Flash Attention, Sample Packing, Gradient Checkpointing, Activation Offloading

Sampling Defaults

temperature: 0.6
top_p: 0.95
top_k: 20