NobodyWho/Qwen_Qwen3.5-0.8B-GGUF
Overview
GGUF quantization of Qwen3.5-0.8B, prepared for NobodyWho: it works with NobodyWho out of the box, with Qwen's recommended sampling metadata embedded in every quant, and is verified with NobodyWho's test suite. The smallest Qwen3.5 model — natively multimodal (text + image), ideal for fast, low-memory on-device chat and tool calling.
Model Capabilities
- Text generation — instruction-following chat
- Tool calling — native function calling with grammar-constrained output (13/14 on NobodyWho's suite)
- Vision — image understanding via the companion
mmproj-BF16.ggufprojection model - Reasoning — thinking mode (on by default)
- Long context — up to 256k tokens
- Multilingual — broad language coverage
Available Quantizations
| File | Approach | Tool-calling tests |
|---|---|---|
Qwen_Qwen3.5-0.8B-BF16-vendor-sampling.gguf | Vendor sampling injected | 13/14 |
Qwen_Qwen3.5-0.8B-Q8_0-vendor-sampling.gguf | Vendor sampling injected | 13/14 |
Qwen_Qwen3.5-0.8B-Q4_K_M-vendor-sampling.gguf | Vendor sampling injected | 13/14 |
mmproj-BF16.gguf | Vision projection (use with any of the above) | — |
Verified with NobodyWho's tool-calling suite across BF16 / Q8_0 / Q4_K_M (13/14 each, June 2026 — smallest model, the same one complex-schema miss at every precision); vision, reasoning, and multilingual verified per-model. The upstream GGUF has no
general.sampling.*metadata, so the-vendor-samplingfiles embed Qwen's recommended sampler (seeINJECTION.md).
Quick Start
Using the NobodyWho library:
from nobodywho import Chat
chat = Chat("huggingface:NobodyWho/Qwen_Qwen3.5-0.8B-GGUF/Qwen_Qwen3.5-0.8B-Q4_K_M-vendor-sampling.gguf")
response = chat.ask("What is the capital of Denmark?").completed()
print(response) # The capital of Denmark is Copenhagen.
Vision
from nobodywho import Model, Chat, Prompt, Image, Text
model = Model(
"huggingface:NobodyWho/Qwen_Qwen3.5-0.8B-GGUF/Qwen_Qwen3.5-0.8B-Q4_K_M-vendor-sampling.gguf",
projection_model_path="huggingface:NobodyWho/Qwen_Qwen3.5-0.8B-GGUF/mmproj-BF16.gguf",
)
chat = Chat(model=model, system_prompt="You are a helpful assistant.")
response = chat.ask(Prompt([
Text("What is in this image?"),
Image("./photo.png"),
])).completed()
print(response)
llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="NobodyWho/Qwen_Qwen3.5-0.8B-GGUF",
filename="Qwen_Qwen3.5-0.8B-Q4_K_M-vendor-sampling.gguf",
)
Model Specifications
- Parameters: 0.8B
- Context length: 262,144 tokens (256K)
- License: Apache 2.0
- Base model: Qwen/Qwen3.5-0.8B
- Architecture: qwen35 (vision-capable)
Licensing / Credits
Licensed under Apache 2.0 (unchanged from upstream). All model credit belongs to the Qwen team, Alibaba Group. GGUF quantizations provided by unsloth.