---

license: apache-2.0

base_model: Qwen/Qwen3.5-0.8B

tags:

- gguf

- nobodywho

- tool-calling

- vision

- qwen

pipeline_tag: image-text-to-text

library_name: gguf

---

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.gguf projection 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-sampling files embed Qwen's recommended sampler (see INJECTION.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.