Instructions to use jmarceno/Elixir-Qwen2.5-Coder-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jmarceno/Elixir-Qwen2.5-Coder-3B with Transformers:

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

pipe = pipeline("text-generation", model="jmarceno/Elixir-Qwen2.5-Coder-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jmarceno/Elixir-Qwen2.5-Coder-3B")
model = AutoModelForCausalLM.from_pretrained("jmarceno/Elixir-Qwen2.5-Coder-3B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jmarceno/Elixir-Qwen2.5-Coder-3B 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 jmarceno/Elixir-Qwen2.5-Coder-3B:Q8_0
# Run inference directly in the terminal:
llama cli -hf jmarceno/Elixir-Qwen2.5-Coder-3B:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jmarceno/Elixir-Qwen2.5-Coder-3B:Q8_0
# Run inference directly in the terminal:
llama cli -hf jmarceno/Elixir-Qwen2.5-Coder-3B: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 jmarceno/Elixir-Qwen2.5-Coder-3B:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf jmarceno/Elixir-Qwen2.5-Coder-3B: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 jmarceno/Elixir-Qwen2.5-Coder-3B:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jmarceno/Elixir-Qwen2.5-Coder-3B:Q8_0

Use Docker

docker model run hf.co/jmarceno/Elixir-Qwen2.5-Coder-3B:Q8_0

LM Studio
Jan

vLLM

How to use jmarceno/Elixir-Qwen2.5-Coder-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jmarceno/Elixir-Qwen2.5-Coder-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jmarceno/Elixir-Qwen2.5-Coder-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jmarceno/Elixir-Qwen2.5-Coder-3B:Q8_0

SGLang

How to use jmarceno/Elixir-Qwen2.5-Coder-3B 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 "jmarceno/Elixir-Qwen2.5-Coder-3B" \
    --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": "jmarceno/Elixir-Qwen2.5-Coder-3B",
		"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 "jmarceno/Elixir-Qwen2.5-Coder-3B" \
        --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": "jmarceno/Elixir-Qwen2.5-Coder-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use jmarceno/Elixir-Qwen2.5-Coder-3B with Ollama:
```
ollama run hf.co/jmarceno/Elixir-Qwen2.5-Coder-3B:Q8_0
```
Unsloth Desktop

How to use jmarceno/Elixir-Qwen2.5-Coder-3B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jmarceno/Elixir-Qwen2.5-Coder-3B: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": "jmarceno/Elixir-Qwen2.5-Coder-3B:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jmarceno/Elixir-Qwen2.5-Coder-3B with Docker Model Runner:
```
docker model run hf.co/jmarceno/Elixir-Qwen2.5-Coder-3B:Q8_0
```

Lemonade

How to use jmarceno/Elixir-Qwen2.5-Coder-3B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jmarceno/Elixir-Qwen2.5-Coder-3B:Q8_0

Run and chat with the model

lemonade run user.Elixir-Qwen2.5-Coder-3B-Q8_0

List all available models

lemonade list

Hermes Agent

How to use jmarceno/Elixir-Qwen2.5-Coder-3B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jmarceno/Elixir-Qwen2.5-Coder-3B: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 jmarceno/Elixir-Qwen2.5-Coder-3B:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jmarceno/Elixir-Qwen2.5-Coder-3B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jmarceno/Elixir-Qwen2.5-Coder-3B: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 "jmarceno/Elixir-Qwen2.5-Coder-3B: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"

Elixir Qwen2.5 Coder 3B

A small Elixir coding model built from Qwen2.5-Coder-3B-Instruct. One transformer block was added (36 layers to 37, about 3.16B parameters), then the model was fine-tuned for Elixir. It is meant to run locally: the Q8 GGUF is a little over 3 GB.

Modified derivative of Qwen2.5-Coder-3B-Instruct. The architecture and weights in this repository are not the original Qwen release. Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved. See LICENSE and NOTICE.

Elixir coding results

Scored on a 128-task executable harness. Each task asks for one Elixir module. A pass means the completion parses, compiles, and its tests pass on Elixir 1.20.2 / OTP 29. One completion per task, temperature 0, 16,384-token context, llama.cpp. Both models below are Q8_0 GGUF, scored in the same run.

	Qwen2.5-Coder-3B-Instruct	Elixir Qwen2.5 Coder 3B	Change
Tasks passed	31 / 128	45 / 128	+14
Pass rate	24.2%	35.2%	+10.9 pp
Relative gain			+45%

Where the gain shows up

Area	Base	Elixir Qwen2.5 Coder 3B
Binaries	0 / 16	5 / 16
Abstractions	5 / 16	9 / 16
Core language	3 / 16	6 / 16
Collections	4 / 16	6 / 16

Warning-free completions rise from 60.2% to 74.2%, and the output-format rate reaches 100%.

Run it

llama.cpp

llama-server -m Elixir-Qwen2.5-Coder-3B-Q8_0.gguf -c 16384 --temp 0.7 --top-k 20 --top-p 0.8 --repeat-penalty 1.05

The benchmark file is Elixir-Qwen2.5-Coder-3B-Q8_0.gguf.

Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "jmarceno/Elixir-Qwen2.5-Coder-3B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": "Write an Elixir module Counter with a GenServer that stores an integer and supports increment and get.",
    }
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

License

Research and evaluation use follows the Qwen Research License. Commercial use of the Qwen materials needs a separate license from Alibaba Cloud. This repository redistributes those materials with the extra layer and the Elixir fine-tune described above.

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Safetensors

Model size

3B params

Tensor type

BF16

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