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Model: reaperdoesntknow/DiStil-Qwen3-1.7B-uncensored Source: Original Platform
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README.md
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---
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base_model:
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- reaperdoesntknow/TopologicalQwen
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tags:
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- convergentintel
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- edge
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- distillation
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- knowledge-distillation
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---
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# DiStil-Qwen3-1.7B-uncensored
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**Uncensored Distillation of Qwen3-1.7B — Alignment-Free Capability Transfer**
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*Convergent Intelligence LLC: Research Division*
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---
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## What This Is
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DiStil-Qwen3-1.7B-uncensored is a 1.7B parameter model produced by distilling Qwen3 with uncensored SFT data, removing alignment-imposed refusal behaviors while preserving the base model's reasoning and generation capabilities. The goal is a model that responds to the prompt as given rather than filtering through safety heuristics that often misfire on legitimate technical, analytical, and research queries.
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This is the base model in a distillation chain:
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- **DiStil-Qwen3-1.7B-uncensored** ← you are here
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- → [Disctil-Qwen3-1.7B](https://huggingface.co/reaperdoesntknow/Disctil-Qwen3-1.7B) (DISC-informed refinement)
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## Architecture
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| Parameter | Value |
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|-----------|-------|
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| Architecture | Qwen3ForCausalLM |
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| Parameters | ~2.03B (1.7B effective) |
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| Hidden Size | 2048 |
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| Layers | 28 |
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| Attention Heads | 16 (Q) / 8 (KV) — GQA |
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| Intermediate | 6144 |
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| Context Length | 40,960 tokens |
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| Vocabulary | 151,936 |
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## Training
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Supervised fine-tuning using TRL on uncensored instruction data. The training preserves the base Qwen3 architecture and tokenizer while shifting the model's response distribution away from refusal patterns. No architectural modifications — this is a pure SFT intervention on the response surface.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"reaperdoesntknow/DiStil-Qwen3-1.7B-uncensored",
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/DiStil-Qwen3-1.7B-uncensored")
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messages = [{"role": "user", "content": "Explain the tradeoffs between alignment training and capability preservation in small language models."}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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output = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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## Mathematical Foundations: Discrepancy Calculus (DISC)
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This model is part of a distillation chain built on Discrepancy Calculus — a measure-theoretic framework where the teacher's output distribution is decomposed via the Mesh Fundamental Identity into smooth (AC), jump, and Cantor components. The discrepancy operator $Df(x) = \lim_{\varepsilon \downarrow 0} \frac{1}{\varepsilon} \int_x^{x+\varepsilon} \frac{|f(t) - f(x)|}{|t - x|} dt$ quantifies local structural mismatch that standard KL divergence averages away.
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Full theory: *"On the Formal Analysis of Discrepancy Calculus"* (Colca, 2026; Convergent Intelligence LLC: Research Division). Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org/10.57967/hf/8165).
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## Related Models
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| Model | Description | Downloads |
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|-------|-------------|-----------|
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| [Disctil-Qwen3-1.7B](https://huggingface.co/reaperdoesntknow/Disctil-Qwen3-1.7B) | DISC-informed refinement of this model | 286 |
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| [DistilQwen3-1.7B-uncensored](https://huggingface.co/reaperdoesntknow/DistilQwen3-1.7B-uncensored) | Parallel distillation variant | 351 |
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| [DistilQwen3-1.7B-uncensored-GGUF](https://huggingface.co/reaperdoesntknow/DistilQwen3-1.7B-uncensored-GGUF) | Quantized for edge deployment | 239 |
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| [TopologicalQwen](https://huggingface.co/reaperdoesntknow/TopologicalQwen) | Topology-aware distillation (TKD) | 622 |
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**[DistilQwen Collection](https://huggingface.co/collections/reaperdoesntknow/distilqwen-69bf40ec669117e3f069ef1c)** — Full proof-weighted distillation series
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Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org/10.57967/hf/8165)
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## Citation
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```bibtex
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@misc{colca2026distiluncensored,
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title={DiStil-Qwen3-1.7B-uncensored: Alignment-Free Capability Transfer},
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author={Colca, Roy S.},
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year={2026},
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publisher={HuggingFace},
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url={https://huggingface.co/reaperdoesntknow/DiStil-Qwen3-1.7B-uncensored},
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note={Convergent Intelligence LLC: Research Division}
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}
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```
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---
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<!-- CIX-CROSSLINK-START -->
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---
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## From the Convergent Intelligence Portfolio
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**[DistilQwen Collection](https://huggingface.co/collections/reaperdoesntknow/distilqwen-69bf40ec669117e3f069ef1c)** — Our only BF16 series. Proof-weighted distillation from Qwen3-30B-A3B → 1.7B and 0.6B on H100. Three teacher variants (Instruct, Thinking, Coder), nine models, 2,788 combined downloads. The rest of the portfolio proves structure beats scale on CPU. This collection shows what happens when you give the methodology real hardware.
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Top model: [Qwen3-1.7B-Coder-Distilled-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) — 508 downloads
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Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org/10.57967/hf/8165)
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*Convergent Intelligence LLC: Research Division*
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<!-- CIX-CROSSLINK-END -->
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*Convergent Intelligence LLC: Research Division*
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*"Where classical analysis fails to see, we begin."*
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---
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<sub>Part of the [reaperdoesntknow research portfolio](https://huggingface.co/reaperdoesntknow) — 49 models, 22,598 total downloads | Last refreshed: 2026-03-30 12:05 UTC</sub>
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<!-- cix-keeper-ts:2026-06-12T13:15:14Z -->
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<!-- card-refresh: 2026-03-30 -->
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---
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*Last updated: 2026-03-31 by Convergent Intelligence LLC: Research Division*
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chat_template.jinja
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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\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 6144,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 40960,
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"max_window_layers": 28,
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"model_type": "qwen3",
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "5.0.0",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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events.out.tfevents.1774711157.c23bde7bdfe8.10063.0
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3
events.out.tfevents.1774711157.c23bde7bdfe8.10063.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:ab24fbf638a33240d9c5e8f5608c78d6f02ac58fac33035ff17642a9127a5969
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size 201489
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generation_config.json
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{
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"temperature": 0.6,
|
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"top_k": 20,
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"top_p": 0.95,
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"transformers_version": "5.0.0"
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}
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version https://git-lfs.github.com/spec/v1
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oid sha256:120d82c4313a59c34bdc26106821e29f5df3f96fe5c3e1234e05c92454e5bb70
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size 4063515640
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3
tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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size 11422650
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
|
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"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
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"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
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"<|quad_end|>",
|
||||
"<|vision_start|>",
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"<|vision_end|>",
|
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"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
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],
|
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"is_local": false,
|
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"model_max_length": 131072,
|
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"pad_token": "<|endoftext|>",
|
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"split_special_tokens": false,
|
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"tokenizer_class": "Qwen2Tokenizer",
|
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"unk_token": null
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}
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5176
trainer_state (2).json
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5176
trainer_state (2).json
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