Instructions to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-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 andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-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 andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M

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 andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M

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 andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M

Use Docker

docker model run hf.co/andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-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": "andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M

Ollama
How to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF with Ollama:
```
ollama run hf.co/andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M
```

Unsloth Studio

How to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-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 andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-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 andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF to start chatting

How to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M

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": "andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M

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 "andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M" \
  --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 andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF with Docker Model Runner:
```
docker model run hf.co/andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M
```

Lemonade

How to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-35B-A3B-telco-tracka-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-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 andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M

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 andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Qwen3.5-35B-A3B — Telco Track A GGUF

GGUF quantizations of a distilled Qwen3.5-35B-A3B fine-tuned on 5G NR drive-test troubleshooting trajectories (Zindi Telco Troubleshooting Agentic Challenge — Track A).

The LoRA adapter was merged into the base weights before quantization.

Files

File	Quantization	Size	Notes
`Qwen3.5-35B-A3B.Q4_K_M.gguf`	Q4_K_M	~20 GB	Recommended — good quality/size trade-off
`Qwen3.5-35B-A3B.Q5_K_M.gguf`	Q5_K_M	~17 GB	Higher quality
`Qwen3.5-35B-A3B.BF16-*.gguf`	BF16 (sharded)	~66 GB	Full precision — re-quantize yourself
`*-mmproj.gguf`	BF16	~861 MB	Multimodal projection (Unsloth artefact)

Model Details

Property	Value
Base model	unsloth/Qwen3.5-35B-A3B
Architecture	Qwen3.5 MoE — 35B total params, ~3B active
Distillation teacher	DeepSeek-V4-Flash
LoRA config (pre-merge)	r=16, alpha=32, BF16, targets: q/k/v/o_proj
Task	5G NR drive-test fault diagnosis (multi-choice)
Quantization tool	Unsloth `save_pretrained_gguf`

How to Run (llama.cpp)

# Q4_K_M — ~24 GB VRAM or RAM
llama-cli -m Qwen3.5-35B-A3B.Q4_K_M.gguf \
    --chat-template qwen3 \
    -p "You are a 5G NR troubleshooting expert..." \
    -n 512

# Q5_K_M
llama-cli -m Qwen3.5-35B-A3B.Q5_K_M.gguf \
    --chat-template qwen3 \
    -n 512

LoRA Adapter

The raw LoRA adapter (without merging) is available at: andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora

Downloads last month: 28

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

5-bit

16-bit

Model tree for andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen3.5-35B-A3B

Finetuned

unsloth/Qwen3.5-35B-A3B

Quantized

(1)

this model