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Model: Surpem/Supertron2-1.7B Source: Original Platform
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97
README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- Qwen/Qwen3-1.7B
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- reasoning
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- math
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- coding
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- instruction-tuned
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- pytorch
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---
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# **Supertron2-1.7B: A Compact, Efficient Instruction-Tuned Language Model**
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## **Model Description**
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**Supertron2-1.7B** is an instruction-tuned language model built on top of Qwen3-1.7B. Designed to be a **reliable, efficient daily driver**, it delivers strong performance across math, coding, reasoning, science, general knowledge, and general conversation while remaining lightweight enough to run on consumer hardware.
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* **Developed by:** Surpem
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* **Model type:** Causal Language Model
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* **Architecture:** Dense Transformer, 1.7B parameters
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* **Fine-tuned from:** [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B)
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* **License:** Apache 2.0
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---
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## **Capabilities**
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### **Reasoning**
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Supertron2-1.7B is designed for clear multi-step reasoning, making it capable of breaking down complex problems in a structured and useful way. It can work through questions methodically rather than jumping directly to a final answer.
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### **Math**
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The model handles a range of math tasks, from arithmetic and algebra to word problems and structured problem solving. It is useful for explaining steps, checking calculations, and producing concise final answers.
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### **Coding**
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Supertron2-1.7B can write, debug, and explain code across popular languages including Python, JavaScript, C++, and more. It understands syntax, common programming patterns, algorithmic reasoning, and practical implementation details.
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### **Science & General Knowledge**
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Broad instruction tuning across science, STEM, and general knowledge domains means the model can hold technical conversations, explain difficult concepts clearly, and assist with research, writing, and analysis tasks.
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### **Instruction Following**
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The model is responsive to natural language instructions. Whether you need concise answers, detailed explanations, structured output, or creative writing, Supertron2-1.7B adapts to the format and tone you ask for without needing complex prompting tricks.
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---
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## **Get Started**
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "Surpem/Supertron2-1.7B"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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messages = [
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{"role": "user", "content": "Explain the difference between LoRA and full fine-tuning."}
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]
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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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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
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```
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---
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## **Hardware Requirements**
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| Precision | Min VRAM | Recommended |
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|---|---|---|
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| bfloat16 | 5 GB | 8 GB+ |
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| 4-bit quantized | 3 GB | 4 GB+ |
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For 4-bit quantized inference:
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```python
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from transformers import BitsAndBytesConfig
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bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)
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model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map="auto")
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```
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---
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## **Citation**
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```bibtex
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@misc{surpem2026supertron2-1.7b,
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title={Supertron2-1.7B — Efficient Instruction-Tuned Language Model},
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author={Surpem},
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year={2026},
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url={https://huggingface.co/Surpem/Supertron2-1.7B},
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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\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,
|
||||||
|
"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": {
|
||||||
|
"rope_theta": 1000000,
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||||||
|
"rope_type": "default"
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|
},
|
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|
"sliding_window": null,
|
||||||
|
"tie_word_embeddings": true,
|
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|
"transformers_version": "5.8.0.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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generation_config.json
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generation_config.json
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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,
|
||||||
|
"top_p": 0.95,
|
||||||
|
"transformers_version": "5.8.0.dev0"
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|
}
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model.safetensors
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model.safetensors
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|
version https://git-lfs.github.com/spec/v1
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|
oid sha256:8c973518270243f4aaf0d472869f92ea26b11ad0f134c30a02e46da373869487
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|
size 3441185608
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tokenizer.json
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tokenizer.json
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|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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||||||
|
size 11422650
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30
tokenizer_config.json
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tokenizer_config.json
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|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"bos_token": null,
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"extra_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>",
|
||||||
|
"<|object_ref_start|>",
|
||||||
|
"<|object_ref_end|>",
|
||||||
|
"<|box_start|>",
|
||||||
|
"<|box_end|>",
|
||||||
|
"<|quad_start|>",
|
||||||
|
"<|quad_end|>",
|
||||||
|
"<|vision_start|>",
|
||||||
|
"<|vision_end|>",
|
||||||
|
"<|vision_pad|>",
|
||||||
|
"<|image_pad|>",
|
||||||
|
"<|video_pad|>"
|
||||||
|
],
|
||||||
|
"is_local": false,
|
||||||
|
"local_files_only": false,
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user