Instructions to use techwithsergiu/Qwen3.5-text-0.8B-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 techwithsergiu/Qwen3.5-text-0.8B-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 techwithsergiu/Qwen3.5-text-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf techwithsergiu/Qwen3.5-text-0.8B-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 techwithsergiu/Qwen3.5-text-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf techwithsergiu/Qwen3.5-text-0.8B-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 techwithsergiu/Qwen3.5-text-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf techwithsergiu/Qwen3.5-text-0.8B-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 techwithsergiu/Qwen3.5-text-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf techwithsergiu/Qwen3.5-text-0.8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/techwithsergiu/Qwen3.5-text-0.8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use techwithsergiu/Qwen3.5-text-0.8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "techwithsergiu/Qwen3.5-text-0.8B-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": "techwithsergiu/Qwen3.5-text-0.8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/techwithsergiu/Qwen3.5-text-0.8B-GGUF:Q4_K_M

Ollama
How to use techwithsergiu/Qwen3.5-text-0.8B-GGUF with Ollama:
```
ollama run hf.co/techwithsergiu/Qwen3.5-text-0.8B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use techwithsergiu/Qwen3.5-text-0.8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf techwithsergiu/Qwen3.5-text-0.8B-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": "techwithsergiu/Qwen3.5-text-0.8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use techwithsergiu/Qwen3.5-text-0.8B-GGUF with Docker Model Runner:
```
docker model run hf.co/techwithsergiu/Qwen3.5-text-0.8B-GGUF:Q4_K_M
```

Lemonade

How to use techwithsergiu/Qwen3.5-text-0.8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull techwithsergiu/Qwen3.5-text-0.8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-text-0.8B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use techwithsergiu/Qwen3.5-text-0.8B-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 techwithsergiu/Qwen3.5-text-0.8B-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 techwithsergiu/Qwen3.5-text-0.8B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use techwithsergiu/Qwen3.5-text-0.8B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf techwithsergiu/Qwen3.5-text-0.8B-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 "techwithsergiu/Qwen3.5-text-0.8B-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"

techwithsergiu commited on Mar 17

Commit

d10f238

verified ·

1 Parent(s): 44ea131

docs: upd diagrams, new qwen-qlora-train, qwen35-toolkit links

Browse files

Files changed (3) hide show

README.md +3 -3
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README.md CHANGED Viewed

@@ -96,7 +96,7 @@ Track the bug at [llama.cpp issues](https://github.com/ggml-org/llama.cpp/issues
 ## From fine-tuned adapter to GGUF
 If you have a LoRA adapter trained with
-[qwen-qlora-train](https://github.com/techwithsergiu/qwen-qlora-train),
 merge it first, then convert to GGUF:
 ```bash
@@ -119,11 +119,11 @@ python llama.cpp/convert_hf_to_gguf.py merged/qwen35-text-0.8B-sft-f16 \
 ```
 Full post-training workflow is documented in
-[qwen-qlora-train → Post-merge workflow](https://github.com/techwithsergiu/qwen-qlora-train#post-merge-workflow).
 ## Conversion
-Converted using [qwen35-toolkit](https://github.com/techwithsergiu/qwen35-toolkit) —
 a Python toolkit for BNB quantization, visual tower removal, verification and
 HF Hub publishing of Qwen3.5 models.

 ## From fine-tuned adapter to GGUF
 If you have a LoRA adapter trained with
+[qwen-qlora-train](https://techwithsergiu.github.io/qwen-qlora-train),
 merge it first, then convert to GGUF:
 ```bash
 ```
 Full post-training workflow is documented in
+[qwen-qlora-train → Post-merge workflow](https://techwithsergiu.github.io/qwen-qlora-train/post-merge-workflow.html).
 ## Conversion
+Converted using [qwen35-toolkit](https://techwithsergiu.github.io/qwen35-toolkit) —
 a Python toolkit for BNB quantization, visual tower removal, verification and
 HF Hub publishing of Qwen3.5 models.

diagrams/diagram_01.png CHANGED Viewed

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Pointer size: 131 Bytes
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Git LFS Details

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Git LFS Details

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Pointer size: 131 Bytes
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Git LFS Details

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