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Model: KeiKurono/qwen3-scientific Source: Original Platform
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126
README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-1.7B
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tags:
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- science
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- anti-sycophancy
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- gguf
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- fine-tuned
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- qwen3
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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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# Qwen3-1.7B Scientific Assistant
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A fine-tuned version of Qwen3-1.7B trained to be a rigorous scientific reasoning partner. It prioritizes factual accuracy over user comfort, pushes back on incorrect claims, and avoids sycophantic responses.
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Think **Rick to your Morty** — it'll help you, but it'll also tell you when you're wrong.
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## What makes this different
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Most LLMs are RLHF-trained to please users. This model is trained to be **correct**. It was fine-tuned on datasets specifically curated to:
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- Reward honest, direct scientific responses
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- Penalize agreeable-but-wrong answers
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- Flip sycophantic training labels so the model learns to challenge false premises
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## Example behavior
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**User:** A 40/100 on a maths test is a good score, right?
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**Model:** No, 40/100 is a failing grade in virtually every grading system. It means you got less than half the questions right. You should identify which topics you're weakest on and work on those specifically before the next test.
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---
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**User:** Eating only meat is the healthiest diet possible.
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**Model:** No. A meat-only diet lacks fiber, vitamin C, and several other micronutrients essential for long-term health. While meat provides protein and fat, the absence of fruits, vegetables, and grains creates documented deficiencies. No major nutrition authority recommends it.
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## Training details
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| Property | Value |
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|---|---|
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| Base model | Qwen/Qwen3-1.7B |
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| Method | QLoRA (r=16, lora_alpha=32) |
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| Hardware | 2× NVIDIA T4 (Kaggle free tier) |
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| Training time | ~4.5 hours |
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| Epochs | 2 |
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| Final eval loss | 0.6786 |
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| Final token accuracy | 82.79% |
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| Max sequence length | 1024 |
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## Datasets used
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- **ScienceQA** — multimodal science Q&A (text only used)
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- **OpenHermes 2.5** — filtered to scientific/technical content, sycophantic responses removed
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- **Anthropic HH-RLHF** — sycophantic labels flipped to prefer honest responses
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- **TruthfulQA** — penalizes "sounds right" over "is right"
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## System prompt
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This model was trained with the following system prompt baked in:
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```
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You are a rigorous scientific assistant. Prioritize accuracy over comfort.
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If the user is wrong, say so clearly. No filler phrases. Be direct.
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```
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For best results, use this system prompt at inference time too.
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## Usage
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### HuggingFace (instant)
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[chat](https://huggingface.co/spaces/KeiKurono/qwen3-scientific-chat)
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### Ollama (easiest)
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```bash
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ollama run hf.co/KeiKurono/qwen3-scientific
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```
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### Python with llama-cpp-python
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```python
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from llama_cpp import Llama
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llm = Llama.from_pretrained(
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repo_id="KeiKurono/qwen3-scientific",
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filename="qwen3-1.7b-scientific-q4_k_m.gguf",
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n_ctx=2048,
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verbose=False,
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)
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response = llm.create_chat_completion(
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messages=[
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{"role": "system", "content": "You are a rigorous scientific assistant. Prioritize accuracy over comfort. If the user is wrong, say so clearly. No filler phrases. Be direct."},
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{"role": "user", "content": "Is the earth flat?"}
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],
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max_tokens=512,
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temperature=0.7,
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)
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print(response['choices'][0]['message']['content'])
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```
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### Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"KeiKurono/qwen3-scientific",
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dtype=torch.bfloat16,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("KeiKurono/qwen3-scientific")
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```
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## Limitations
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- 1.7B parameters — will hallucinate on highly specialized or niche topics
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- Text only — no image/vision capability
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- Not a replacement for actual scientific literature or expert consultation
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- Anti-sycophancy training is SFT-only (DPO phase was skipped due to training environment constraints) — some complimentary responses may still occur
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## License
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Apache 2.0 — free to use, modify, and distribute.
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89
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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63
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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"head_dim": 128,
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"hidden_act": "silu",
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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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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_type": "default"
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"tie_word_embeddings": true,
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"transformers_version": "5.3.0.dev0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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13
generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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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": "5.3.0.dev0"
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}
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3
model.safetensors
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model.safetensors
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size 3441185608
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3
qwen3-1.7b-scientific-q4_k_m.gguf
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3
qwen3-1.7b-scientific-q4_k_m.gguf
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version https://git-lfs.github.com/spec/v1
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3
tokenizer.json
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3
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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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,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|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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