Image-Text-to-Text
Transformers
Safetensors
English
qwen3_vl
vision-language
new-yorker
humor
rlhf
sft-thinking
conversational
Instructions to use HumorR1/policy-e1b-sft-thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HumorR1/policy-e1b-sft-thinking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="HumorR1/policy-e1b-sft-thinking") 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, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("HumorR1/policy-e1b-sft-thinking") model = AutoModelForImageTextToText.from_pretrained("HumorR1/policy-e1b-sft-thinking") 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
- vLLM
How to use HumorR1/policy-e1b-sft-thinking with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HumorR1/policy-e1b-sft-thinking" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HumorR1/policy-e1b-sft-thinking", "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/HumorR1/policy-e1b-sft-thinking
- SGLang
How to use HumorR1/policy-e1b-sft-thinking 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 "HumorR1/policy-e1b-sft-thinking" \ --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": "HumorR1/policy-e1b-sft-thinking", "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 "HumorR1/policy-e1b-sft-thinking" \ --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": "HumorR1/policy-e1b-sft-thinking", "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 HumorR1/policy-e1b-sft-thinking with Docker Model Runner:
docker model run hf.co/HumorR1/policy-e1b-sft-thinking
upload E1b (sft_thinking)
Browse files- .gitattributes +1 -0
- README.md +60 -0
- chat_template.jinja +110 -0
- config.json +66 -0
- generation_config.json +14 -0
- model.safetensors +3 -0
- processor_config.json +60 -0
- tokenizer.json +3 -0
- tokenizer_config.json +30 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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@@ -0,0 +1,60 @@
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| 1 |
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---
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| 2 |
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license: apache-2.0
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base_model: Qwen/Qwen3-VL-2B-Thinking
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library_name: transformers
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tags:
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- vision-language
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- new-yorker
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- humor
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- rlhf
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- sft-thinking
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datasets:
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- yguooo/newyorker_caption_ranking
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language:
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- en
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---
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# humor-r1 — SFT, with thinking (Qwen3-VL-2B-Thinking + LoRA, merged) (E1b)
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LoRA-adapted Qwen3-VL-2B-Thinking supervised fine-tuned on (image, synthetic thinking, chosen caption) triples, then merged. Output format: `{thinking}</think>\n\n<caption>X</caption>`.
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## Training data
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| 22 |
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- 271 New Yorker contests, top-rated caption per contest
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(`yguooo/newyorker_caption_ranking`).
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- The 60k Bradley-Terry preference pairs underlying the reward model
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(separate split).
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- We deliberately do NOT use the dataset's GPT-4o-generated
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Scene/Twist/Location/Entities descriptions in the prompt, since they
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hand-feed scene content to a vision-language model that can already
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see the image; this makes the policy and reward model usable on any
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single-panel cartoon, not just the curated subset.
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+
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## How it fits the project
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Part of a 2x2 ablation over training method (SFT, GRPO) and output
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format (no thinking, thinking) for humor caption generation. See
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`HumorR1/rm-qwen25vl-3b-nodesc` for the reward model used to train (and
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| 38 |
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score) this policy.
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| 39 |
+
|
| 40 |
+
## Inference
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| 41 |
+
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| 42 |
+
Backbone: `Qwen/Qwen3-VL-2B-Thinking`.
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This repo is a merged full model; load with `transformers.AutoModelForCausalLM.from_pretrained`.
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+
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| 45 |
+
```python
|
| 46 |
+
from PIL import Image
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| 47 |
+
from transformers import AutoProcessor
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| 48 |
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from vllm import LLM, SamplingParams
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| 49 |
+
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| 50 |
+
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processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-2B-Thinking", trust_remote_code=True)
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| 52 |
+
llm = LLM(model="Qwen/Qwen3-VL-2B-Thinking", trust_remote_code=True, dtype="bfloat16",
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| 53 |
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max_model_len=4096)
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| 55 |
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# Caption format: <caption>X</caption>; thinking variant prefixes <think>...</think>.
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| 56 |
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```
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| 58 |
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## Reward model used during training
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| 59 |
+
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| 60 |
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- `HumorR1/rm-qwen25vl-3b-nodesc` (held-out pairwise accuracy 0.6635).
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chat_template.jinja
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@@ -0,0 +1,110 @@
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| 1 |
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{%- set image_count = namespace(value=0) %}
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| 2 |
+
{%- set video_count = namespace(value=0) %}
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| 3 |
+
{%- macro render_content(content, do_vision_count) %}
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| 4 |
+
{%- if content is string %}
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| 5 |
+
{{- content }}
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| 6 |
+
{%- else %}
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| 7 |
+
{%- for item in content %}
|
| 8 |
+
{%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
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| 9 |
+
{%- if do_vision_count %}
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| 10 |
+
{%- set image_count.value = image_count.value + 1 %}
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| 11 |
+
{%- endif %}
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| 12 |
+
{%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
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| 13 |
+
<|vision_start|><|image_pad|><|vision_end|>
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| 14 |
+
{%- elif 'video' in item or item.type == 'video' %}
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| 15 |
+
{%- if do_vision_count %}
|
| 16 |
+
{%- set video_count.value = video_count.value + 1 %}
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| 17 |
+
{%- endif %}
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| 18 |
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{%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
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| 19 |
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<|vision_start|><|video_pad|><|vision_end|>
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| 20 |
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{%- elif 'text' in item %}
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| 21 |
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{{- item.text }}
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| 22 |
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{%- endif %}
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| 23 |
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{%- endfor %}
|
| 24 |
+
{%- endif %}
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| 25 |
+
{%- endmacro %}
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| 26 |
+
{%- if tools %}
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| 27 |
+
{{- '<|im_start|>system\n' }}
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| 28 |
+
{%- if messages[0].role == 'system' %}
|
| 29 |
+
{{- render_content(messages[0].content, false) + '\n\n' }}
|
| 30 |
+
{%- endif %}
|
| 31 |
+
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
| 32 |
+
{%- for tool in tools %}
|
| 33 |
+
{{- "\n" }}
|
| 34 |
+
{{- tool | tojson }}
|
| 35 |
+
{%- endfor %}
|
| 36 |
+
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
| 37 |
+
{%- else %}
|
| 38 |
+
{%- if messages[0].role == 'system' %}
|
| 39 |
+
{{- '<|im_start|>system\n' + render_content(messages[0].content, false) + '<|im_end|>\n' }}
|
| 40 |
+
{%- endif %}
|
| 41 |
+
{%- endif %}
|
| 42 |
+
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
| 43 |
+
{%- for message in messages[::-1] %}
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| 44 |
+
{%- set index = (messages|length - 1) - loop.index0 %}
|
| 45 |
+
{%- if ns.multi_step_tool and message.role == "user" %}
|
| 46 |
+
{%- set content = render_content(message.content, false) %}
|
| 47 |
+
{%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
|
| 48 |
+
{%- set ns.multi_step_tool = false %}
|
| 49 |
+
{%- set ns.last_query_index = index %}
|
| 50 |
+
{%- endif %}
|
| 51 |
+
{%- endif %}
|
| 52 |
+
{%- endfor %}
|
| 53 |
+
{%- for message in messages %}
|
| 54 |
+
{%- set content = render_content(message.content, True) %}
|
| 55 |
+
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
| 56 |
+
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
|
| 57 |
+
{%- elif message.role == "assistant" %}
|
| 58 |
+
{%- set reasoning_content = '' %}
|
| 59 |
+
{%- if message.reasoning_content is string %}
|
| 60 |
+
{%- set reasoning_content = message.reasoning_content %}
|
| 61 |
+
{%- else %}
|
| 62 |
+
{%- if '</think>' in content %}
|
| 63 |
+
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
| 64 |
+
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
|
| 65 |
+
{%- endif %}
|
| 66 |
+
{%- endif %}
|
| 67 |
+
{%- if loop.index0 > ns.last_query_index %}
|
| 68 |
+
{%- if loop.last or (not loop.last and reasoning_content) %}
|
| 69 |
+
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
| 70 |
+
{%- else %}
|
| 71 |
+
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 72 |
+
{%- endif %}
|
| 73 |
+
{%- else %}
|
| 74 |
+
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 75 |
+
{%- endif %}
|
| 76 |
+
{%- if message.tool_calls %}
|
| 77 |
+
{%- for tool_call in message.tool_calls %}
|
| 78 |
+
{%- if (loop.first and content) or (not loop.first) %}
|
| 79 |
+
{{- '\n' }}
|
| 80 |
+
{%- endif %}
|
| 81 |
+
{%- if tool_call.function %}
|
| 82 |
+
{%- set tool_call = tool_call.function %}
|
| 83 |
+
{%- endif %}
|
| 84 |
+
{{- '<tool_call>\n{"name": "' }}
|
| 85 |
+
{{- tool_call.name }}
|
| 86 |
+
{{- '", "arguments": ' }}
|
| 87 |
+
{%- if tool_call.arguments is string %}
|
| 88 |
+
{{- tool_call.arguments }}
|
| 89 |
+
{%- else %}
|
| 90 |
+
{{- tool_call.arguments | tojson }}
|
| 91 |
+
{%- endif %}
|
| 92 |
+
{{- '}\n</tool_call>' }}
|
| 93 |
+
{%- endfor %}
|
| 94 |
+
{%- endif %}
|
| 95 |
+
{{- '<|im_end|>\n' }}
|
| 96 |
+
{%- elif message.role == "tool" %}
|
| 97 |
+
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
| 98 |
+
{{- '<|im_start|>user' }}
|
| 99 |
+
{%- endif %}
|
| 100 |
+
{{- '\n<tool_response>\n' }}
|
| 101 |
+
{{- content }}
|
| 102 |
+
{{- '\n</tool_response>' }}
|
| 103 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 104 |
+
{{- '<|im_end|>\n' }}
|
| 105 |
+
{%- endif %}
|
| 106 |
+
{%- endif %}
|
| 107 |
+
{%- endfor %}
|
| 108 |
+
{%- if add_generation_prompt %}
|
| 109 |
+
{{- '<|im_start|>assistant\n<think>\n' }}
|
| 110 |
+
{%- endif %}
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config.json
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|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Qwen3VLForConditionalGeneration"
|
| 4 |
+
],
|
| 5 |
+
"dtype": "bfloat16",
|
| 6 |
+
"image_token_id": 151655,
|
| 7 |
+
"model_type": "qwen3_vl",
|
| 8 |
+
"text_config": {
|
| 9 |
+
"attention_bias": false,
|
| 10 |
+
"attention_dropout": 0.0,
|
| 11 |
+
"bos_token_id": 151643,
|
| 12 |
+
"dtype": "bfloat16",
|
| 13 |
+
"eos_token_id": 151645,
|
| 14 |
+
"head_dim": 128,
|
| 15 |
+
"hidden_act": "silu",
|
| 16 |
+
"hidden_size": 2048,
|
| 17 |
+
"initializer_range": 0.02,
|
| 18 |
+
"intermediate_size": 6144,
|
| 19 |
+
"max_position_embeddings": 262144,
|
| 20 |
+
"model_type": "qwen3_vl_text",
|
| 21 |
+
"num_attention_heads": 16,
|
| 22 |
+
"num_hidden_layers": 28,
|
| 23 |
+
"num_key_value_heads": 8,
|
| 24 |
+
"pad_token_id": null,
|
| 25 |
+
"rms_norm_eps": 1e-06,
|
| 26 |
+
"rope_parameters": {
|
| 27 |
+
"mrope_interleaved": true,
|
| 28 |
+
"mrope_section": [
|
| 29 |
+
24,
|
| 30 |
+
20,
|
| 31 |
+
20
|
| 32 |
+
],
|
| 33 |
+
"rope_theta": 5000000,
|
| 34 |
+
"rope_type": "default"
|
| 35 |
+
},
|
| 36 |
+
"tie_word_embeddings": true,
|
| 37 |
+
"use_cache": true,
|
| 38 |
+
"vocab_size": 151936
|
| 39 |
+
},
|
| 40 |
+
"tie_word_embeddings": true,
|
| 41 |
+
"transformers_version": "5.5.4",
|
| 42 |
+
"video_token_id": 151656,
|
| 43 |
+
"vision_config": {
|
| 44 |
+
"deepstack_visual_indexes": [
|
| 45 |
+
5,
|
| 46 |
+
11,
|
| 47 |
+
17
|
| 48 |
+
],
|
| 49 |
+
"depth": 24,
|
| 50 |
+
"dtype": "bfloat16",
|
| 51 |
+
"hidden_act": "gelu_pytorch_tanh",
|
| 52 |
+
"hidden_size": 1024,
|
| 53 |
+
"in_channels": 3,
|
| 54 |
+
"initializer_range": 0.02,
|
| 55 |
+
"intermediate_size": 4096,
|
| 56 |
+
"model_type": "qwen3_vl",
|
| 57 |
+
"num_heads": 16,
|
| 58 |
+
"num_position_embeddings": 2304,
|
| 59 |
+
"out_hidden_size": 2048,
|
| 60 |
+
"patch_size": 16,
|
| 61 |
+
"spatial_merge_size": 2,
|
| 62 |
+
"temporal_patch_size": 2
|
| 63 |
+
},
|
| 64 |
+
"vision_end_token_id": 151653,
|
| 65 |
+
"vision_start_token_id": 151652
|
| 66 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token_id": 151643,
|
| 3 |
+
"do_sample": true,
|
| 4 |
+
"eos_token_id": [
|
| 5 |
+
151645,
|
| 6 |
+
151643
|
| 7 |
+
],
|
| 8 |
+
"pad_token_id": 151643,
|
| 9 |
+
"repetition_penalty": 1.0,
|
| 10 |
+
"temperature": 1.0,
|
| 11 |
+
"top_k": 20,
|
| 12 |
+
"top_p": 0.95,
|
| 13 |
+
"transformers_version": "5.5.4"
|
| 14 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3d95d6c80b67d52f413bb3d2982251103e39c2f2d107218017a4dee346880e52
|
| 3 |
+
size 4255140312
|
processor_config.json
ADDED
|
@@ -0,0 +1,60 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"image_processor": {
|
| 3 |
+
"do_convert_rgb": true,
|
| 4 |
+
"do_normalize": true,
|
| 5 |
+
"do_rescale": true,
|
| 6 |
+
"do_resize": true,
|
| 7 |
+
"image_mean": [
|
| 8 |
+
0.5,
|
| 9 |
+
0.5,
|
| 10 |
+
0.5
|
| 11 |
+
],
|
| 12 |
+
"image_processor_type": "Qwen2VLImageProcessor",
|
| 13 |
+
"image_std": [
|
| 14 |
+
0.5,
|
| 15 |
+
0.5,
|
| 16 |
+
0.5
|
| 17 |
+
],
|
| 18 |
+
"merge_size": 2,
|
| 19 |
+
"patch_size": 16,
|
| 20 |
+
"resample": 3,
|
| 21 |
+
"rescale_factor": 0.00392156862745098,
|
| 22 |
+
"size": {
|
| 23 |
+
"longest_edge": 16777216,
|
| 24 |
+
"shortest_edge": 65536
|
| 25 |
+
},
|
| 26 |
+
"temporal_patch_size": 2
|
| 27 |
+
},
|
| 28 |
+
"processor_class": "Qwen3VLProcessor",
|
| 29 |
+
"video_processor": {
|
| 30 |
+
"do_convert_rgb": true,
|
| 31 |
+
"do_normalize": true,
|
| 32 |
+
"do_rescale": true,
|
| 33 |
+
"do_resize": true,
|
| 34 |
+
"do_sample_frames": true,
|
| 35 |
+
"fps": 2,
|
| 36 |
+
"image_mean": [
|
| 37 |
+
0.5,
|
| 38 |
+
0.5,
|
| 39 |
+
0.5
|
| 40 |
+
],
|
| 41 |
+
"image_std": [
|
| 42 |
+
0.5,
|
| 43 |
+
0.5,
|
| 44 |
+
0.5
|
| 45 |
+
],
|
| 46 |
+
"max_frames": 768,
|
| 47 |
+
"merge_size": 2,
|
| 48 |
+
"min_frames": 4,
|
| 49 |
+
"patch_size": 16,
|
| 50 |
+
"resample": 3,
|
| 51 |
+
"rescale_factor": 0.00392156862745098,
|
| 52 |
+
"return_metadata": false,
|
| 53 |
+
"size": {
|
| 54 |
+
"longest_edge": 25165824,
|
| 55 |
+
"shortest_edge": 4096
|
| 56 |
+
},
|
| 57 |
+
"temporal_patch_size": 2,
|
| 58 |
+
"video_processor_type": "Qwen3VLVideoProcessor"
|
| 59 |
+
}
|
| 60 |
+
}
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
|
| 3 |
+
size 11422650
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"backend": "tokenizers",
|
| 4 |
+
"bos_token": null,
|
| 5 |
+
"clean_up_tokenization_spaces": false,
|
| 6 |
+
"eos_token": "<|im_end|>",
|
| 7 |
+
"errors": "replace",
|
| 8 |
+
"extra_special_tokens": [
|
| 9 |
+
"<|im_start|>",
|
| 10 |
+
"<|im_end|>",
|
| 11 |
+
"<|object_ref_start|>",
|
| 12 |
+
"<|object_ref_end|>",
|
| 13 |
+
"<|box_start|>",
|
| 14 |
+
"<|box_end|>",
|
| 15 |
+
"<|quad_start|>",
|
| 16 |
+
"<|quad_end|>",
|
| 17 |
+
"<|vision_start|>",
|
| 18 |
+
"<|vision_end|>",
|
| 19 |
+
"<|vision_pad|>",
|
| 20 |
+
"<|image_pad|>",
|
| 21 |
+
"<|video_pad|>"
|
| 22 |
+
],
|
| 23 |
+
"is_local": false,
|
| 24 |
+
"model_max_length": 262144,
|
| 25 |
+
"pad_token": "<|endoftext|>",
|
| 26 |
+
"processor_class": "Qwen3VLProcessor",
|
| 27 |
+
"split_special_tokens": false,
|
| 28 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 29 |
+
"unk_token": null
|
| 30 |
+
}
|