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
Download vllm-patch/apply.sh from Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound: direct link, hf CLI and curl.
- Browser
- Download file 3.14 kB
-
https://huggingface.co/Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound/resolve/main/vllm-patch/apply.sh
- Command line
-
hf download hf://Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound/vllm-patch/apply.sh
-
curl -L -o apply.sh https://huggingface.co/Minachist/Qwen3.8-27B-INT6-Flat-6.6bpw-AutoRound/resolve/main/vllm-patch/apply.sh
3.14 kB
| # 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." | |