Instructions to use aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf",
	filename="Algocean-Qwen3.6-35B-A3B.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 aisamdasu/Algocean-Qwen3.6-35B-A3B.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 aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf
# Run inference directly in the terminal:
llama cli -hf aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf
# Run inference directly in the terminal:
llama cli -hf aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf

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 aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf
# Run inference directly in the terminal:
./llama-cli -hf aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf

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 aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf

Use Docker

docker model run hf.co/aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf

LM Studio
Jan

vLLM

How to use aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf

Ollama
How to use aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf with Ollama:
```
ollama run hf.co/aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf
```

Unsloth Studio

How to use aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf 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 aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf 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 aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf to start chatting

How to use aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf

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": "aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use aisamdasu/Algocean-Qwen3.6-35B-A3B.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 aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf

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 aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf

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 "aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf" \
  --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 aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf with Docker Model Runner:
```
docker model run hf.co/aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf
```

Lemonade

How to use aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf

Run and chat with the model

lemonade run user.Algocean-Qwen3.6-35B-A3B.gguf-{{QUANT_TAG}}

List all available models

lemonade list

Algocean-Qwen3.6-35B-A3B.gguf

Merged GGUF F16 release of Qwen/Qwen3.6-35B-A3B fine-tuned with a LoRA SFT run on the aisamdasu/algocean-fable5-traces trace mix.

The published GGUF uses checkpoint 3600, which had the lowest validation loss in the training run.

Use

llama-cli -m Algocean-Qwen3.6-35B-A3B.gguf -p "Create a concise LangGraph plan for a repo refactor." -n 512

Artifact

item	value
Format	GGUF F16
File	`Algocean-Qwen3.6-35B-A3B.gguf`
File size	71,066,993,920 bytes
GGUF version	3
GGUF metadata keys	43
GGUF tensors	753
LoRA tensors merged	20
SHA-256	`564448e3c3861fd9f143bba4311c4a3cdc92da6709099fa25d5ed1cc0a4c3e89`

Training

key	value
Base model	`Qwen/Qwen3.6-35B-A3B`
Dataset	`aisamdasu/algocean-fable5-traces`
Train / eval rows	30,265 / 512
Selected source rows	Crownelius 26,938 + kelexine 3,839
Dedup skipped	1,721
Fine-tune method	LoRA SFT
LoRA target modules	`q_proj`, `v_proj`
LoRA rank / alpha / dropout	16 / 32 / 0.0
Max sequence length	16,384
Optimizer / schedule	AdamW fused / cosine
Learning rate / warmup	1e-5 / 0.03
Batch / grad accumulation	1 / 1
Max steps	4,000
Eval / save interval	200 / 200
GPU	Modal B200 x1
Runtime	7,934.71 sec

Checkpoint Selection

step	eval loss	eval PPL	note
200	1.300712	3.671911	first eval
3200	1.067631	2.908480	near plateau
3400	1.067996	2.909543	regression
3600	1.066911	2.906388	selected best
3800	1.067495	2.908084	worse than best
4000	1.067005	2.906661	final, slightly worse

Best checkpoint rationale:

3600 has the lowest validation loss: 1.066911.
Final step 4000 is very close but still higher: +0.000094 eval loss.
The curve flattened after 3200, so selecting 3600 keeps the best validation point without chasing extra train loss.

Metrics

metric	value
Best eval loss	1.066911
Best eval perplexity	2.906388
Final eval loss	1.067005
Final eval perplexity	2.906661
Trainer average train loss	0.494604
Last logged train loss	0.996137
Train samples/sec	0.504
Train steps/sec	0.504
Eval samples/sec at final eval	2.088

Comparison Notes

This release is the best checkpoint from the completed Algocean LoRA run. It is not claiming a public benchmark win over the base model or the upstream OptiQ model; those require the same held-out evaluation harness on all models. The selection here is based on the run's validation loss, where checkpoint 3600 is the lowest-loss point among all saved checkpoints.

Intended Use

This model is intended for local llama.cpp-compatible inference experiments around agentic coding, tool-use traces, and LangGraph-style planning. It is not a general safety-tuned assistant release.

Downloads last month: 265

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

(171)

this model

aisamdasu
/

Algocean-Qwen3.6-35B-A3B.gguf

Algocean-Qwen3.6-35B-A3B.gguf

Use

Artifact

Training

Checkpoint Selection

Metrics

Comparison Notes

Intended Use

Model tree for aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf

Dataset used to train aisamdasu/Algocean-Qwen3.6-35B-A3B.gguf