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"

Qwen3.5-text-0.8B-GGUF / README.md

techwithsergiu

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

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	---
	tags:
	- techwithsergiu
	- gguf
	- qwen3_5_text
	library_name: gguf
	license: apache-2.0
	license_link: https://huggingface.co/Qwen/Qwen3.5-0.8B/blob/main/LICENSE
	pipeline_tag: text-generation
	base_model:
	- techwithsergiu/Qwen3.5-text-0.8B
	---

	# Qwen3.5-text-0.8B-GGUF

	<img width="400px" src="https://qianwen-res.oss-accelerate.aliyuncs.com/logo_qwen3.5.png">

	GGUF quants of [techwithsergiu/Qwen3.5-text-0.8B](https://huggingface.co/techwithsergiu/Qwen3.5-text-0.8B) —
	the text-only bf16 derivative of [Qwen/Qwen3.5-0.8B](https://huggingface.co/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

	![](diagrams/diagram_01.png)

	\| Model \| Type \| Base model \|
	\|---\|---\|---\|
	\| [Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) \| f16 · VLM · source \| — \|
	\| [techwithsergiu/Qwen3.5-0.8B-bnb-4bit](https://huggingface.co/techwithsergiu/Qwen3.5-0.8B-bnb-4bit) \| BNB NF4 · VLM \| Qwen/Qwen3.5-0.8B \|
	\| [techwithsergiu/Qwen3.5-text-0.8B](https://huggingface.co/techwithsergiu/Qwen3.5-text-0.8B) \| bf16 · text-only \| Qwen/Qwen3.5-0.8B \|
	\| [techwithsergiu/Qwen3.5-text-0.8B-bnb-4bit](https://huggingface.co/techwithsergiu/Qwen3.5-text-0.8B-bnb-4bit) \| BNB NF4 · text-only \| Qwen3.5-text-0.8B \|
	\| [techwithsergiu/Qwen3.5-text-0.8B-GGUF](https://huggingface.co/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

	```bash
	./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](https://lmstudio.ai).
	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](https://github.com/ggml-org/llama.cpp/issues).

	```bash
	# 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

	![](diagrams/diagram_02.png)

	## 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
	# 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](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.

	---

	## Acknowledgements

	Based on [Qwen/Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B)
	by the Qwen Team. If you use this model in research, please cite the original:

	```bibtex
	@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}
	}
	```