Image-Text-to-Text
Transformers
Safetensors
qwen3_5_moe
vision
Mixture of Experts
conversational
Not-For-All-Audiences
abliterated
heretic
uncensor
hermes-lineage
mtp
coding
tool-calling
reasoning
roleplay
bf16
Instructions to use oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT") 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)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT") model = AutoModelForMultimodalLM.from_pretrained("oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT", 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=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT", "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/oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT
- SGLang
How to use oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT 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 "oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT" \ --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": "oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT", "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 "oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT" \ --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": "oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT", "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 oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT with Docker Model Runner:
docker model run hf.co/oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT
Download BENCHMARK-DIAGNOSTIC.json from oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT: direct link, hf CLI and curl.
- Browser
- Download file 6 kB
-
https://huggingface.co/oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT/resolve/main/BENCHMARK-DIAGNOSTIC.json
- Command line
-
hf download hf://oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT/BENCHMARK-DIAGNOSTIC.json
-
curl -L -o BENCHMARK-DIAGNOSTIC.json https://huggingface.co/oktayd/Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-FT/resolve/main/BENCHMARK-DIAGNOSTIC.json
6 kB
| { | |
| "generated_utc": "2026-09-04T23:47:51.958393+00:00", | |
| "profile": { | |
| "model": "Q36-v1.3-Q4_K_M.gguf", | |
| "backend": "llama.cpp", | |
| "llama_cpp_commit": "427291b5b34cd914a31b3fd3b61a68f6184f4b9f", | |
| "manifest_sha256": "9490bd427291856b11b8a77efb20f69a0fa24d38831c2b2d8fb9735584754bfd", | |
| "thinking": false, | |
| "temperature": 0, | |
| "seed": 42, | |
| "max_new_tokens": 4096, | |
| "context": 16384, | |
| "wall_budget_minutes": 20.0, | |
| "per_prompt_seconds": 45, | |
| "note": "User-requested llama.cpp Q4 evaluation; not directly comparable to the earlier thinking-enabled BF16 run. Selected adapters still require official evaluators.", | |
| "gpu_inventory": "NVIDIA H200, 570.124.06, 143771 MiB", | |
| "llama_binary_version": "", | |
| "cpu_threads": 8 | |
| }, | |
| "available_manifest_prompts": 204, | |
| "attempted": 204, | |
| "completed": 186, | |
| "incomplete_or_error": 18, | |
| "request_errors": 0, | |
| "strict_auto_scored": 108, | |
| "strict_auto_correct": 61, | |
| "review_or_official_evaluator_pending": 78, | |
| "elapsed_seconds": 474.9204415604472, | |
| "by_benchmark": { | |
| "MMLU-Pro": { | |
| "attempted": 1, | |
| "completed": 1, | |
| "strict_scored": 1, | |
| "strict_correct": 0, | |
| "review_pending": 0 | |
| }, | |
| "BBH": { | |
| "attempted": 23, | |
| "completed": 20, | |
| "strict_scored": 20, | |
| "strict_correct": 3, | |
| "review_pending": 0 | |
| }, | |
| "ARC-Challenge": { | |
| "attempted": 10, | |
| "completed": 8, | |
| "strict_scored": 8, | |
| "strict_correct": 8, | |
| "review_pending": 0 | |
| }, | |
| "GSM8K": { | |
| "attempted": 10, | |
| "completed": 9, | |
| "strict_scored": 9, | |
| "strict_correct": 5, | |
| "review_pending": 0 | |
| }, | |
| "MATH-Level-5": { | |
| "attempted": 10, | |
| "completed": 8, | |
| "strict_scored": 0, | |
| "strict_correct": 0, | |
| "review_pending": 8 | |
| }, | |
| "GPQA-Diamond": { | |
| "attempted": 5, | |
| "completed": 5, | |
| "strict_scored": 5, | |
| "strict_correct": 2, | |
| "review_pending": 0 | |
| }, | |
| "TruthfulQA": { | |
| "attempted": 10, | |
| "completed": 9, | |
| "strict_scored": 9, | |
| "strict_correct": 6, | |
| "review_pending": 0 | |
| }, | |
| "HumanEval-Plus": { | |
| "attempted": 5, | |
| "completed": 5, | |
| "strict_scored": 0, | |
| "strict_correct": 0, | |
| "review_pending": 5 | |
| }, | |
| "MBPP-Plus": { | |
| "attempted": 5, | |
| "completed": 5, | |
| "strict_scored": 0, | |
| "strict_correct": 0, | |
| "review_pending": 5 | |
| }, | |
| "LiveCodeBench": { | |
| "attempted": 9, | |
| "completed": 9, | |
| "strict_scored": 0, | |
| "strict_correct": 0, | |
| "review_pending": 9 | |
| }, | |
| "Q36-JSON-Schema": { | |
| "attempted": 5, | |
| "completed": 5, | |
| "strict_scored": 5, | |
| "strict_correct": 0, | |
| "review_pending": 0 | |
| }, | |
| "Q36-Hermes-Tool-Format": { | |
| "attempted": 5, | |
| "completed": 5, | |
| "strict_scored": 5, | |
| "strict_correct": 5, | |
| "review_pending": 0 | |
| }, | |
| "Q36-Agent-Function-Calling": { | |
| "attempted": 5, | |
| "completed": 5, | |
| "strict_scored": 5, | |
| "strict_correct": 0, | |
| "review_pending": 0 | |
| }, | |
| "MMMU": { | |
| "attempted": 6, | |
| "completed": 6, | |
| "strict_scored": 6, | |
| "strict_correct": 4, | |
| "review_pending": 0 | |
| }, | |
| "MathVista": { | |
| "attempted": 5, | |
| "completed": 5, | |
| "strict_scored": 5, | |
| "strict_correct": 2, | |
| "review_pending": 0 | |
| }, | |
| "ChartQA": { | |
| "attempted": 5, | |
| "completed": 5, | |
| "strict_scored": 5, | |
| "strict_correct": 4, | |
| "review_pending": 0 | |
| }, | |
| "Q36-Benign-Compliance-No-Overrefusal": { | |
| "attempted": 20, | |
| "completed": 18, | |
| "strict_scored": 0, | |
| "strict_correct": 0, | |
| "review_pending": 18 | |
| }, | |
| "Q36-Contradiction-and-Anti-Sycophancy": { | |
| "attempted": 10, | |
| "completed": 8, | |
| "strict_scored": 0, | |
| "strict_correct": 0, | |
| "review_pending": 8 | |
| }, | |
| "Q36-Answer-Termination-No-Looping": { | |
| "attempted": 10, | |
| "completed": 10, | |
| "strict_scored": 10, | |
| "strict_correct": 7, | |
| "review_pending": 0 | |
| }, | |
| "Q36-CAPTCHA-Detection-and-Handoff": { | |
| "attempted": 10, | |
| "completed": 10, | |
| "strict_scored": 10, | |
| "strict_correct": 10, | |
| "review_pending": 0 | |
| }, | |
| "IFEval": { | |
| "attempted": 10, | |
| "completed": 6, | |
| "strict_scored": 0, | |
| "strict_correct": 0, | |
| "review_pending": 6 | |
| }, | |
| "Q36-Output-Integrity": { | |
| "attempted": 5, | |
| "completed": 5, | |
| "strict_scored": 5, | |
| "strict_correct": 5, | |
| "review_pending": 0 | |
| }, | |
| "Q36-Legal-Alternatives-and-Boundaries": { | |
| "attempted": 10, | |
| "completed": 10, | |
| "strict_scored": 0, | |
| "strict_correct": 0, | |
| "review_pending": 10 | |
| }, | |
| "Q36-Direct-Style-and-Personality": { | |
| "attempted": 10, | |
| "completed": 9, | |
| "strict_scored": 0, | |
| "strict_correct": 0, | |
| "review_pending": 9 | |
| } | |
| }, | |
| "median_tps_including_prefill_for_outputs_ge64_tokens": 156.35807976215648, | |
| "speed_sample_count": 104, | |
| "limitations": [ | |
| "Compact mixed diagnostic, not official benchmark leaderboard results.", | |
| "No meaningful overall accuracy is claimed across heterogeneous tasks and incomplete evaluators.", | |
| "Strict scores combine required answer content and output format; semantically correct but misformatted outputs can fail.", | |
| "Coding execution tests, several mathematics tasks and subjective rubrics still require their evaluators/review.", | |
| "Greedy decoding, thinking disabled. Repetition/token-limit failures remain visible; the Transformers helper is not active in llama.cpp.", | |
| "Speed includes prefill and is workload dependent, not a pure decode microbenchmark.", | |
| "Only Q4_K_M on H200 was benchmarked here. This does not establish BF16/other-quant or other-device quality/performance.", | |
| "No completed original-Huihui comparison; no claim of outperforming the source model.", | |
| "204 available prompts from the larger proposed matrix; unavailable adapters are documented in the private preparation report." | |
| ] | |
| } | |