Text Generation
Transformers
Safetensors
English
Japanese
qwen3_5
image-text-to-text
qwen3.5
reasoning
efficient-thinking
vision-validation-evaluated
full-weights
text-evaluated
conversational
Instructions to use horiuchinobuyuki/Qwick-3.5-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use horiuchinobuyuki/Qwick-3.5-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="horiuchinobuyuki/Qwick-3.5-9B") 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("horiuchinobuyuki/Qwick-3.5-9B") model = AutoModelForMultimodalLM.from_pretrained("horiuchinobuyuki/Qwick-3.5-9B", 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 horiuchinobuyuki/Qwick-3.5-9B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "horiuchinobuyuki/Qwick-3.5-9B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "horiuchinobuyuki/Qwick-3.5-9B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/horiuchinobuyuki/Qwick-3.5-9B
- SGLang
How to use horiuchinobuyuki/Qwick-3.5-9B 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 "horiuchinobuyuki/Qwick-3.5-9B" \ --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": "horiuchinobuyuki/Qwick-3.5-9B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "horiuchinobuyuki/Qwick-3.5-9B" \ --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": "horiuchinobuyuki/Qwick-3.5-9B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use horiuchinobuyuki/Qwick-3.5-9B with Docker Model Runner:
docker model run hf.co/horiuchinobuyuki/Qwick-3.5-9B
| { | |
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| "attempted_once": true, | |
| "author": { | |
| "name": "Nobuyuki Horiuchi", | |
| "profile": "https://huggingface.co/horiuchinobuyuki" | |
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| "completion_tokens": { | |
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| "finish_reason_counts": { | |
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| "stop": 286 | |
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| "p50": 1050.0, | |
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| "candidate_frozen_before_access": true, | |
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| "dataset": { | |
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| "randopt_source_split_randopt_search": true, | |
| "reserved_before_training_and_search_windows": true, | |
| "training_manifest_held_out_test_used_false": true, | |
| "training_source_split_distill_train": true | |
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| "generator_version": "sft5-diverse-synthetic-v3", | |
| "name": "SFT5 genre-v3 one-shot release holdout", | |
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| "inventory_ledger_ja": 22, | |
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| "selected_ids_sha256": "sha256:c87cf96566a31216257595b9bf32874f8bb21c91422b1d422bfd8fabc59a7ae7", | |
| "semantic_fingerprints_sha256": "sha256:3be2d319cf591e78d9455caa5e443936043638a186a8355593580279a108af12", | |
| "source_sha256": "sha256:6fea94d642c5fac144639be7d99a47c803eec8065e5b11cedb53e7acb680252b", | |
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| "eligibility_audit_sha256": "sha256:336e39619eb1778a3af516b8b08f8ceaa122ee26c58b7b35ff5232318759ebfc", | |
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| "candidate_and_policy_immutable": true, | |
| "failure_action": "publish failure transparently or stop release; do not optimize against this holdout", | |
| "no_candidate_selection_or_hyperparameter_change_after_open": true, | |
| "report_all_rows_and_subgroups": true | |
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| "decode": { | |
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| "max_tokens": 8192, | |
| "presence_penalty": 0.0, | |
| "seed": 2026081801, | |
| "temperature": 0.0, | |
| "top_k": -1, | |
| "top_p": 1.0, | |
| "trace_policy": "full upstream reasoning plus final content durably persisted; merged full trace graded" | |
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| "budget_correct_definition": "raw correct && finish_reason != length", | |
| "grader": "qet.grading.grade_explicit_final_output over merged full trace", | |
| "primary": "budget_accuracy", | |
| "secondary": [ | |
| "raw_accuracy", | |
| "format_rate", | |
| "finish_reason_counts", | |
| "completion_tokens_mean_p50_p90", | |
| "language_grader_task_family_difficulty_subgroups", | |
| "paired_correctness_transitions", | |
| "paired_bootstrap_95pct_ci" | |
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| "all_required": true, | |
| "each_grader_budget_accuracy_delta_min": -0.05, | |
| "each_language_budget_accuracy_delta_min": -0.04, | |
| "interpretation": "pass only if every gate passes; disclose every result; no post-holdout retuning", | |
| "mean_completion_token_ratio_max": 0.7, | |
| "overall_budget_accuracy_delta_min": -0.02, | |
| "trace_integrity_and_384_matched_rows": true | |
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| "sha256": "sha256:8a929e01ec7f5d60a5b14ff6664f74ce4d86b216d26155ca613e371183ef5701" | |
| }, | |
| "kind": "qet_g7_frozen_internal_release_gate_results", | |
| "measurement_interpretation": "The predeclared temperature-zero decode is valid for this frozen internal acceptance rule. It is not the temperature-1.0 measurement profile used for public benchmark claims and must not be compared as though it were.", | |
| "paired_bootstrap": { | |
| "budget_accuracy_delta_95pct_ci": [ | |
| 0.125, | |
| 0.20833333333333334 | |
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| "mean_completion_token_ratio_95pct_ci": [ | |
| 0.4463466424570172, | |
| 0.5239079217022228 | |
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| "resamples": 10000, | |
| "role": "report_only", | |
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| "post_result_retuning": false, | |
| "public_model": { | |
| "name": "Qwick-3.5-9B", | |
| "repo_id": "horiuchinobuyuki/Qwick-3.5-9B", | |
| "url": "https://huggingface.co/horiuchinobuyuki/Qwick-3.5-9B" | |
| }, | |
| "public_performance_benchmark": false, | |
| "redistribution": "aggregate metrics and identities only; prompts, answer keys, and full traces are not included", | |
| "release_gate_pass": true, | |
| "release_gates": { | |
| "each_grader_budget_accuracy_delta_ge_minus_0_05": true, | |
| "each_language_budget_accuracy_delta_ge_minus_0_04": true, | |
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| "rerun": false, | |
| "role": "frozen internal candidate-acceptance release gate", | |
| "schema_version": 1, | |
| "status": "pass", | |
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| "hard": { | |
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| "candidate": { | |
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| } | |
| }, | |
| "japanese_checksum_reasoning": { | |
| "base": { | |
| "budget_accuracy": 1.0, | |
| "budget_correct": 21, | |
| "completion_tokens": { | |
| "max": 5677, | |
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| "n": 21, | |
| "raw_accuracy": 1.0, | |
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| }, | |
| "candidate": { | |
| "budget_accuracy": 1.0, | |
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| "completion_tokens": { | |
| "max": 3544, | |
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