Text Generation
Transformers
GGUF
PyTorch
code
multiscale_transformer
code-generation
multi-scale-transformer
cpu-optimized
koinic
llama
byte-level
agentic
Eval Results (legacy)
Instructions to use KoinicLabs/AXL-Coder-15M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoinicLabs/AXL-Coder-15M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KoinicLabs/AXL-Coder-15M")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("KoinicLabs/AXL-Coder-15M", dtype="auto") - llama-cpp-python
How to use KoinicLabs/AXL-Coder-15M with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="KoinicLabs/AXL-Coder-15M", filename="axl-coder-llama-f16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use KoinicLabs/AXL-Coder-15M with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf KoinicLabs/AXL-Coder-15M:F16 # Run inference directly in the terminal: llama-cli -hf KoinicLabs/AXL-Coder-15M:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf KoinicLabs/AXL-Coder-15M:F16 # Run inference directly in the terminal: llama-cli -hf KoinicLabs/AXL-Coder-15M:F16
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 KoinicLabs/AXL-Coder-15M:F16 # Run inference directly in the terminal: ./llama-cli -hf KoinicLabs/AXL-Coder-15M:F16
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 KoinicLabs/AXL-Coder-15M:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf KoinicLabs/AXL-Coder-15M:F16
Use Docker
docker model run hf.co/KoinicLabs/AXL-Coder-15M:F16
- LM Studio
- Jan
- vLLM
How to use KoinicLabs/AXL-Coder-15M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KoinicLabs/AXL-Coder-15M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KoinicLabs/AXL-Coder-15M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/KoinicLabs/AXL-Coder-15M:F16
- SGLang
How to use KoinicLabs/AXL-Coder-15M 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 "KoinicLabs/AXL-Coder-15M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KoinicLabs/AXL-Coder-15M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "KoinicLabs/AXL-Coder-15M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KoinicLabs/AXL-Coder-15M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use KoinicLabs/AXL-Coder-15M with Ollama:
ollama run hf.co/KoinicLabs/AXL-Coder-15M:F16
- Unsloth Studio new
How to use KoinicLabs/AXL-Coder-15M 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 KoinicLabs/AXL-Coder-15M 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 KoinicLabs/AXL-Coder-15M to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for KoinicLabs/AXL-Coder-15M to start chatting
- Docker Model Runner
How to use KoinicLabs/AXL-Coder-15M with Docker Model Runner:
docker model run hf.co/KoinicLabs/AXL-Coder-15M:F16
- Lemonade
How to use KoinicLabs/AXL-Coder-15M with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KoinicLabs/AXL-Coder-15M:F16
Run and chat with the model
lemonade run user.AXL-Coder-15M-F16
List all available models
lemonade list
File size: 5,226 Bytes
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<title>AXL-Coder-15M - AXL</title>
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<div class="hero">
<div class="cat SGD">SGD Optimized</div>
<h1>AXL-Coder-15M</h1>
<p class="desc">Agentic coding. 26M params. PPL 1.54. 8-action tool router.</p>
</div>
<div class="ms">
<div class="mc"><div class="v">26M</div><div class="l">Parameters</div></div>
<div class="mc"><div class="v">1.54</div><div class="l">Perplexity</div></div>
<div class="mc"><div class="v">---</div><div class="l">Training</div></div>
<div class="mc"><div class="v">30 MB</div><div class="l">GGUF</div></div>
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<div class="tabs">
<input type="radio" name="t" id="t1" checked><label for="t1" class="tl">Specs</label>
<input type="radio" name="t" id="t2"><label for="t2" class="tl">Training</label>
<input type="radio" name="t" id="t3"><label for="t3" class="tl">Usage</label>
<input type="radio" name="t" id="t4"><label for="t4" class="tl">Download</label>
<div class="p p1">
<table>
<tr><th>Property</th><th>Value</th></tr>
<tr><td>Architecture</td><td>Multi-Scale Transformer</td></tr>
<tr><td>d_model</td><td>?</td></tr>
<tr><td>Attention Heads</td><td>?</td></tr>
<tr><td>Layers per Scale</td><td>?</td></tr>
<tr><td>Context Window</td><td>256 bytes</td></tr>
<tr><td>Downsample Factors</td><td>[1, 2, 4]</td></tr>
<tr><td>Vocab Size</td><td>258 (byte-level)</td></tr>
<tr><td>Optimizer</td><td>SGD</td></tr>
</table>
</div>
<div class="p p2">
<div class="story">Trained with SGD for 10 min. Tool router predicts 8 actions: generate, read, write, edit, run, search, think, done.</div>
<table>
<tr><th>Metric</th><th>Value</th></tr>
<tr><td>Final Loss</td><td>0.4331</td></tr>
<tr><td>Perplexity</td><td>1.54</td></tr>
<tr><td>Training Steps</td><td>?</td></tr>
<tr><td>Training Time</td><td>---</td></tr>
</table>
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<div class="p p3">
<h3 style="color:#fff;margin-bottom:12px">Usage</h3>
<pre><code>ollama create axl-coder-15m -f Modelfile
ollama run axl-coder-15m "def fibonacci():"</code></pre>
<div class="note">Agentic model. Decides what action to take per coding task.</div>
</div>
<div class="p p4">
<table>
<tr><th>File</th><th>Size</th><th>Format</th></tr>
<tr><td>F16 GGUF</td><td>30 MB</td><td>Full precision</td></tr>
<tr><td>Q4_K_M GGUF</td><td>30 MB</td><td>4-bit quantized</td></tr>
</table>
<div class="note" style="margin-top:16px">GGUF files work with Ollama and llama.cpp. Q4_K_M is about 3x smaller than F16.</div>
</div>
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