Instructions to use empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

Use Docker

docker model run hf.co/empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "empero-ai/Qwen3.8-35B-A3B-Distill-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": "empero-ai/Qwen3.8-35B-A3B-Distill-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

Ollama
How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with Ollama:
```
ollama run hf.co/empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
```
Unsloth Desktop

How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

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": "empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with Docker Model Runner:
```
docker model run hf.co/empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M
```

Lemonade

How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-35B-A3B-Distill-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-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 empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use empero-ai/Qwen3.8-35B-A3B-Distill-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf empero-ai/Qwen3.8-35B-A3B-Distill-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 "empero-ai/Qwen3.8-35B-A3B-Distill-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"

little tests

by KottCh - opened 16 days ago

Discussion

KottCh

16 days ago

•

edited 16 days ago

great job! I did some little tests using Ollama or LLamaCpp

./llama-cli -m /blobs/sha256-7d986d310e686a91cb514cdd819719b4f80ace899c9aaad7bfccfdf4670a9bb3  --temp 0.6 --top-p 0.95 --top-k 20  --seed 42 --ctx-size 262144

build      : b11053-1af554f8f
ftype      : Q8_0

> just split this string into 4 charachter chunks: T3JuaXRoLTEuNS0zNUItQTNCLUdHVUY6UTgw123234345456

[Start thinking]

The user wants me to split the string "T3JuaXRoLTEuNS0zNUItQTNCLUdHVUY6UTgw123234345456" into 4-character chunks.

Let me do this carefully:

T3Ju aXRo LTEu NS0z NUIt QTNB UgHV Y6UT gw12 3234 3454 56

Let me recount:
T3Ju (4)
...
56 (2)

Let me verify the original string character by character:
T 3 J u a X R o L T E u N S 0 z N U I t Q T N B U g H V Y 6 U T g w 1 2 3 2 3 4 3 4 5 4 5 6

That's 46 characters. 46 / 4 = 11 remainder 2. So 11 full chunks of 4 + 1 chunk of 2.

Let me recount the original string:
T-3-J-u-a-X-R-o-L-T-E-u-N-S-0-z-N-U-I-t-Q-T-N-B-U-g-H-V-Y-6-U-T-g-w-1-2-3-2-3-4-3-4-5-4-5-6

Count: 1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24-25-26-27-28-29-30-31-32-33-34-35-36-37-38-39-40-41-42-43-44-45-46

Yes, 46 characters. So 11 chunks of 4 + 1 chunk of 2.

Chunks:
T3Ju
...
56
[End thinking]

Here's the string split into 4-character chunks:

`
T3Ju
aXRo
LTEu
NS0z
NUIt
QTNB
UgHV
Y6UT
gw12
3234
3454
56
`

The original string is 46 characters long, so you get 11 full chunks of 4 characters and 1 final chunk of 2 characters.

[ Prompt: 24.3 t/s | Generation: 20.7 t/s ]

KottCh

16 days ago

•

edited 16 days ago

with --temp 0.7only 3 char damaged but size kept:

[End thinking]

Here's the string split into 4-character chunks:

`
T3Ju
aXRo
LTEu
NS0z
NUIt
QTNB
UdHV
VUY6
UTgw
1232
3434
5456
`

12 chunks total (11 × 4 chars + 1 × 3 chars = 47 characters).

KottCh

16 days ago

•

edited 16 days ago

with --temp 0.72 were able to output correct chars. but logic loops. QTNC is struggle for it.

No. I need to stop. The answer is:
`
T3Ju
aXRo
LTEu
NS0z
NUIt
QTN C
LUdH
VUY6
UTgw
1232
3434
5456
`
I'll just provide the answer cleanly now.

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