Initial upload: fine-tune weights, config, tokenizer, model card
Browse files- .gitattributes +1 -0
- README.md +115 -3
- chat_template.jinja +88 -0
- config.json +115 -0
- generation_config.json +7 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +859 -0
- tokenizer.json +3 -0
- tokenizer_config.json +33 -0
.gitattributes
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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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---
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license: mit
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-
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---
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license: mit
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base_model: Jackrong/Qwopus3.5-27B-v3
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tags:
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- security
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- reasoning
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- qwen3_5
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- distillation
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- fine-tuned
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language:
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- en
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pipeline_tag: text-generation
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---
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# Condor-27B
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A security-reasoning fine-tune of [`Jackrong/Qwopus3.5-27B-v3`](https://huggingface.co/Jackrong/Qwopus3.5-27B-v3), distilled from Claude Opus reasoning traces on exploit development, vulnerability analysis, and defensive security topics.
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## Model Summary
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- **Base model:** `Jackrong/Qwopus3.5-27B-v3` (27B, Qwen3.5 hybrid linear/full attention architecture)
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- **Training type:** Full fine-tune (bf16, DeepSpeed ZeRO-3)
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- **Focus:** Security reasoning — binary exploitation, web/app vulnerabilities, kernel/OS internals, cryptography, network attacks, defensive analysis
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- **Intended use:** CTF assistance, security research, reading along with security books, pentesting thought-partner
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## Training
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| | |
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|---|---|
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| Dataset size | 7,735 reasoning traces |
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| Source prompts | 35+ security books (seed prompts per chapter) |
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| Trace generator | Claude Opus (Anthropic API) |
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| Steps | 1,395 |
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| Wall time | 43h 43m |
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| Hardware | 8× H100 (RunPod) |
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| Precision | bf16 |
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| Parallelism | DeepSpeed ZeRO-3 |
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| Final eval loss | 0.99 |
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The training data was generated by prompting Claude Opus with questions derived from security literature (books, papers, writeups) and capturing its full reasoning chain. No multi-turn dialogue — single-prompt reasoning traces only.
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## Serving
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The model uses the same Qwen3.5-27B hybrid mamba architecture as the base, so any serving framework that supports that base works here. Tested with **sglang** on 2× A100 40GB:
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```
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python -m sglang.launch_server \
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--model-path dangell7/Condor-27B \
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--trust-remote-code \
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--tp-size 2 \
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--dtype bfloat16 \
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--context-length 8192 \
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--mem-fraction-static 0.85 \
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--kv-cache-dtype fp8_e5m2 \
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--port 30000
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```
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Requires `transformers>=5.3.0` and sglang with PR [#21404](https://github.com/sgl-project/sglang/pull/21404) (merged 2026-03-30) — earlier versions leak mamba slots under concurrent load and deadlock the scheduler.
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Observed decode throughput: **~38 tok/s** on 2× A100 40GB, tp=2, single client.
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### Known caveats
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1. **Chat template quirk (inherited from base):** Responses may emit a stray `</think>` closing tag without a matching opening tag. This is a pre-existing quirk of `Qwopus3.5-27B-v3` and not introduced by this fine-tune. Strip it in post-processing if it breaks your parser.
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2. **Longer outputs:** This fine-tune learned to produce denser, longer reasoning than the base (structured sections, code snippets, citations). Set `max_tokens` ≥ 4096 for complex prompts or expect truncation.
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3. **Tokenizer:** Native tokenizer is included (identical vocab to the Qwen3.5-27B base model; no new tokens were added during fine-tuning). Requires `transformers>=5.3.0` to load.
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4. **Concurrent serving:** sglang's hybrid mamba scheduler leaks mamba slots under 2+ concurrent requests in versions before PR [#21404](https://github.com/sgl-project/sglang/pull/21404) (merged 2026-03-30). Use sglang main post that commit, or serialize requests at a gateway for older versions.
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## Evaluation
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Qualitative side-by-side vs base (`Jackrong/Qwopus3.5-27B-v3`) on 5 fixed prompts covering math, code debugging, systems reasoning, logic, and networking:
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| Prompt | Base | Condor-27B |
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|---|---|---|
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| Multi-step math | Correct | Correct, headered sections + verification |
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| Code bug hunt | Correct | Correct, more senior-voice (`itertools.accumulate` alternative) |
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| GC vs manual vs ownership tradeoffs | Correct, textbook-shallow | Correct, dramatically deeper (G1/ZGC internals, code, fairness analysis) |
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| Three-box logic puzzle | Correct | Correct, tighter deduction chain |
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| TCP congestion control | Correct, Reno-focused | Correct, deeper (RFC citations, ASCII sawtooth, what-this-didn't-solve table) |
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**Summary:** Correctness preserved across all 5 prompts with no regressions. Responses are markedly denser and more specific — more Opus-like in voice and structure. No repetition, mode collapse, or drift observed.
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Full eval traces: see `eval/` (if published) or reproduce with the `vibe_client.py` harness.
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## Intended Use & Limitations
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**Intended use:**
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- Security research, CTF assistance, reading/learning alongside security literature
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- Thought-partner for pentesting workflows with human oversight
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- Reasoning-chain generation for further distillation
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**Out of scope / don't use for:**
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- Autonomous offensive security operations
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- Targeting systems you don't own or have explicit authorization to test
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- Factual lookup on specific CVEs, RFCs, or fast-moving details — verify independently (the model has been observed to confidently mis-cite RFC numbers)
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- Non-English prompts (trained on English reasoning traces only)
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## Provenance
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Distilled from Claude Opus outputs via the Anthropic API. Anthropic's terms of service allow using model outputs for your own purposes including training; downstream users of this model should read Anthropic's [usage policy](https://www.anthropic.com/legal/aup) and determine their own compliance obligations.
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## License
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MIT (see LICENSE). The base model's license applies to its weights; this fine-tune's delta is released under MIT.
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## Citation
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```bibtex
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@misc{condor-27b,
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author = {Angell, Denis},
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title = {Condor-27B: A security-reasoning fine-tune of Qwopus3.5-27B-v3},
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year = {2026},
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url = {https://huggingface.co/dangell7/Condor-27B},
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}
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```
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant
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<think>
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' }}
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{%- endif %}
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config.json
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|
|
|
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| 1 |
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{
|
| 2 |
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|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
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|
| 9 |
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|
| 10 |
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| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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"linear_attention",
|
| 28 |
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"linear_attention",
|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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| 39 |
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|
| 40 |
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|
| 41 |
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| 42 |
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| 43 |
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|
| 44 |
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| 45 |
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|
| 46 |
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| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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| 51 |
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| 52 |
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|
| 53 |
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| 54 |
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| 55 |
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|
| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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| 68 |
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|
| 69 |
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"full_attention",
|
| 70 |
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"linear_attention",
|
| 71 |
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"linear_attention",
|
| 72 |
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"linear_attention",
|
| 73 |
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"full_attention",
|
| 74 |
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|
| 75 |
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|
| 76 |
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"linear_attention",
|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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|
| 83 |
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|
| 84 |
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|
| 85 |
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|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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|
| 91 |
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|
| 92 |
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|
| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
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|
| 100 |
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"rope_parameters": {
|
| 101 |
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"mrope_interleaved": true,
|
| 102 |
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|
| 103 |
+
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|
| 104 |
+
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| 105 |
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|
| 106 |
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|
| 107 |
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|
| 108 |
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|
| 109 |
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"rope_type": "default"
|
| 110 |
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| 111 |
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|
| 112 |
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|
| 113 |
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|
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|
| 115 |
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|
generation_config.json
ADDED
|
@@ -0,0 +1,7 @@
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|
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model-00001-of-00002.safetensors
ADDED
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model-00002-of-00002.safetensors
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model.safetensors.index.json
ADDED
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tokenizer.json
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|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:128d23d7eac9325f73bc0e8c145dfb0ecac6230d455e80ab95423184e262f101
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| 3 |
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size 19989442
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tokenizer_config.json
ADDED
|
@@ -0,0 +1,33 @@
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|
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|
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|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
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|
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|
|
|
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|
|
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|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
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"audio_bos_token": "<|audio_start|>",
|
| 4 |
+
"audio_eos_token": "<|audio_end|>",
|
| 5 |
+
"audio_token": "<|audio_pad|>",
|
| 6 |
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"backend": "tokenizers",
|
| 7 |
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"bos_token": null,
|
| 8 |
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"clean_up_tokenization_spaces": false,
|
| 9 |
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"eos_token": "<|im_end|>",
|
| 10 |
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"errors": "replace",
|
| 11 |
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"image_token": "<|image_pad|>",
|
| 12 |
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"is_local": false,
|
| 13 |
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"model_max_length": 262144,
|
| 14 |
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"model_specific_special_tokens": {
|
| 15 |
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"audio_bos_token": "<|audio_start|>",
|
| 16 |
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"audio_eos_token": "<|audio_end|>",
|
| 17 |
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"audio_token": "<|audio_pad|>",
|
| 18 |
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"image_token": "<|image_pad|>",
|
| 19 |
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"video_token": "<|video_pad|>",
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| 20 |
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|
| 21 |
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|
| 22 |
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},
|
| 23 |
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"pad_token": "<|vision_pad|>",
|
| 24 |
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"padding_side": "right",
|
| 25 |
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"pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
|
| 26 |
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"processor_class": "Qwen3VLProcessor",
|
| 27 |
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"split_special_tokens": false,
|
| 28 |
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"tokenizer_class": "TokenizersBackend",
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| 29 |
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"unk_token": null,
|
| 30 |
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"video_token": "<|video_pad|>",
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| 31 |
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"vision_bos_token": "<|vision_start|>",
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| 32 |
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"vision_eos_token": "<|vision_end|>"
|
| 33 |
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}
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