Instructions to use techwithsergiu/Qwen3.5-text-0.8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use techwithsergiu/Qwen3.5-text-0.8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="techwithsergiu/Qwen3.5-text-0.8B-GGUF",
	filename="Qwen3.5-text-0.8B-F16.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 techwithsergiu/Qwen3.5-text-0.8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 Studio

How to use techwithsergiu/Qwen3.5-text-0.8B-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 techwithsergiu/Qwen3.5-text-0.8B-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 techwithsergiu/Qwen3.5-text-0.8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for techwithsergiu/Qwen3.5-text-0.8B-GGUF to start chatting

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-server -hf techwithsergiu/Qwen3.5-text-0.8B-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": "techwithsergiu/Qwen3.5-text-0.8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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-server -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 new
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

Qwen3.5-text-0.8B-GGUF

GGUF quants of techwithsergiu/Qwen3.5-text-0.8B — the text-only bf16 derivative of Qwen/Qwen3.5-0.8B.

The visual tower has been removed before conversion. All text-backbone weights are identical to the original — no retraining, no weight changes, no quality loss for text tasks.

Quants

File	Type	Size	Notes
`Qwen3.5-text-0.8B-Q8_0.gguf`	Q8_0	~53% of f16	near-lossless — for high-quality inference
`Qwen3.5-text-0.8B-Q6_K.gguf`	Q6_K	~41% of f16	excellent quality, good balance with f16
`Qwen3.5-text-0.8B-Q5_K_M.gguf`	Q5_K_M	~37% of f16	very good quality, smaller than Q6
`Qwen3.5-text-0.8B-Q4_K_M.gguf`	Q4_K_M	~31% of f16	✅ recommended — best size/quality balance
`Qwen3.5-text-0.8B-Q4_K_S.gguf`	Q4_K_S	~30% of f16	optional — slightly smaller, slightly lower quality

Model family

Model	Type	Base model
Qwen/Qwen3.5-0.8B	f16 · VLM · source	—
techwithsergiu/Qwen3.5-0.8B-bnb-4bit	BNB NF4 · VLM	Qwen/Qwen3.5-0.8B
techwithsergiu/Qwen3.5-text-0.8B	bf16 · text-only	Qwen/Qwen3.5-0.8B
techwithsergiu/Qwen3.5-text-0.8B-bnb-4bit	BNB NF4 · text-only	Qwen3.5-text-0.8B
techwithsergiu/Qwen3.5-text-0.8B-GGUF	GGUF quants	Qwen3.5-text-0.8B

The GGUF repo is derived from the text-only f16 model — same weights, different container format. base_model points to the f16 text variant to keep the VLM and text lineages distinct on the Hub.

Inference

llama.cpp

./llama.cpp/build/bin/llama-cli \
    -m Qwen3.5-text-0.8B-Q4_K_M.gguf \
    -p "What is the capital of Romania?" \
    -n 256

LM Studio

Load any .gguf file from this repo directly in LM Studio. Recommended quant: Q4_K_M.

Thinking mode

Qwen3.5 supports an optional chain-of-thought <think> block before the answer. Thinking is enabled by default in llama.cpp.

Note: --chat-template-kwargs '{"enable_thinking":...}' is deprecated — do not use. Known issue: --reasoning off is accepted but does not actually disable thinking. Workaround: use --reasoning-budget 0 — this reliably disables the <think> block. Track the bug at llama.cpp issues.

# Thinking OFF — direct answer (workaround: --reasoning-budget 0)
./llama.cpp/build/bin/llama-cli \
    -m Qwen3.5-text-0.8B-Q4_K_M.gguf \
    --reasoning-budget 0 \
    -p "What is the capital of Romania?" \
    -n 256

# Thinking ON — default, no flag needed
./llama.cpp/build/bin/llama-cli \
    -m Qwen3.5-text-0.8B-Q4_K_M.gguf \
    -p "What is 17 × 34?" \
    -n 1024

Pipeline diagram

From fine-tuned adapter to GGUF

If you have a LoRA adapter trained with qwen-qlora-train, merge it first, then convert to GGUF:

# 1. Merge adapter into f16 weights
qlora-merge \
  --base  Qwen/Qwen3.5-0.8B \
  --adapter adapters/<run_name> \
  --output merged/qwen35-text-0.8B-sft-f16

# 2. Convert merged model to GGUF  (requires llama.cpp)
python llama.cpp/convert_hf_to_gguf.py merged/qwen35-text-0.8B-sft-f16 \
    --outtype f16 \
    --outfile merged/qwen35-text-0.8B-sft-F16.gguf

# 3. Quantize
./llama.cpp/build/bin/llama-quantize \
    merged/qwen35-text-0.8B-sft-F16.gguf \
    merged/qwen35-text-0.8B-sft-Q4_K_M.gguf \
    Q4_K_M

Full post-training workflow is documented in qwen-qlora-train → Post-merge workflow.

Conversion

Converted using qwen35-toolkit — a Python toolkit for BNB quantization, visual tower removal, verification and HF Hub publishing of Qwen3.5 models.

Acknowledgements

Based on Qwen/Qwen3.5-0.8B by the Qwen Team. If you use this model in research, please cite the original:

@misc{qwen3.5,
    title  = {{Qwen3.5}: Towards Native Multimodal Agents},
    author = {{Qwen Team}},
    month  = {February},
    year   = {2026},
    url    = {https://qwen.ai/blog?id=qwen3.5}
}