Image-Text-to-Text
Transformers
Safetensors
qwen3_5
compressed-tensors
int6
autoround
humming
conversational
Instructions to use Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound") model = AutoModelForMultimodalLM.from_pretrained("Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound
- SGLang
How to use Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound with Docker Model Runner:
docker model run hf.co/Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound
File size: 3,144 Bytes
f0c414f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | #!/usr/bin/env bash
# apply.sh — build bind-mountable copies of vLLM's Qwen3.5 model files with
# quantized-embedding support wired in.
#
# Why this is needed
# -----------------
# vLLM already ships a compressed-tensors quantized-embedding implementation
# (`CompressedTensorsEmbeddingWNA16Int` in
# model_executor/layers/quantization/compressed_tensors/compressed_tensors_embedding.py).
# It is simply never reached for Qwen3.5, because models/qwen3_5.py builds
#
# self.embed_tokens = VocabParallelEmbedding(self.vocab_size, config.hidden_size)
#
# with neither `quant_config` nor `prefix`, so VocabParallelEmbedding falls back to
# `UnquantizedEmbeddingMethod` unconditionally. `ParallelLMHead` in the same file
# does receive `quant_config`, which is why this looks like an oversight rather
# than a design decision.
#
# Without the patch, loading this checkpoint fails with:
#
# ValueError: There is no module or parameter named 'embed_tokens.weight_packed'
# in Qwen3_5Model. The available parameters belonging to embed_tokens
# (VocabParallelEmbedding) are: {'embed_tokens.weight'}
#
# The patch adds two keyword arguments and nothing else. On a checkpoint whose
# embeddings are *not* quantized, `get_scheme_dict` returns None and the layer
# falls back to `UnquantizedEmbeddingMethod` exactly as before, so the change is
# backward compatible.
#
# Usage
# -----
# ./apply.sh [image] # default: the image below
# podman run ... $(cat mounts.txt) <image> --model /model ...
#
# Verified against vLLM 0.26.1rc1.dev542+gb22afe45a on 2026-08-18.
set -euo pipefail
HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
IMAGE="${1:-docker.io/library/vllm-p2p-env-qwen38-27b:latest}"
MODELS_DIR=/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models
RUNTIME="${CONTAINER_RUNTIME:-podman}"
say() { echo "[apply.sh] $*"; }
say "extracting originals from $IMAGE"
"$RUNTIME" run --rm -v "$HERE":/o --entrypoint bash "$IMAGE" -c "
set -e
for f in qwen3_5.py qwen3_5_mtp.py; do
cp $MODELS_DIR/\$f /o/\$f.orig
chmod 666 /o/\$f.orig
done
python3 -c 'import vllm; print(\"vllm\", vllm.__version__)' > /o/.vllm-version
chmod 666 /o/.vllm-version
"
say "image reports $(cat "$HERE/.vllm-version")"
for f in qwen3_5.py qwen3_5_mtp.py; do
cp "$HERE/$f.orig" "$HERE/$f"
# The target is given explicitly, so the a/ b/ paths in the diff header do
# not have to match this directory layout.
if ! patch "$HERE/$f" < "$HERE/$f.patch" >/dev/null; then
say "✗ $f.patch did not apply — vLLM changed upstream."
say " Re-derive it: put the new originals next to make_embed_quant_patch.py,"
say " run it, and diff -u the result. It refuses to guess if the anchor moved."
rm -f "$HERE/$f"
exit 1
fi
say "✓ patched $f"
done
{
for f in qwen3_5.py qwen3_5_mtp.py; do
printf -- '-v %s:%s:ro ' "$HERE/$f" "$MODELS_DIR/$f"
done
echo
} > "$HERE/mounts.txt"
say "wrote $HERE/mounts.txt:"
cat "$HERE/mounts.txt"
say "add those flags to your podman/docker run, or the equivalent volumes: entries in compose."
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