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Model: Divij/Qwen2.5-3B-Instruct-sft-without-thoughts Source: Original Platform
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113
README.md
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---
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base_model: Qwen/Qwen2.5-3B-Instruct
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library_name: transformers
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license: other
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pipeline_tag: text-generation
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tags:
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- sft
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- scientific-reasoning
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- instruction-tuning
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- open-instruct
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---
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# Divij/Qwen2.5-3B-Instruct-sft-without-thoughts
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Supervised fine-tune of [`Qwen/Qwen2.5-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on a scientific-methodology
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instruction dataset, where each assistant response is a plain step-by-step research methodology.
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The project goal is to compare whether including explicit `<Thought_i>` reasoning
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traces alongside each `<Step_i>` action during SFT produces stronger scientific-methodology
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generators than training on step-only plans.
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## Variant
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This checkpoint is the **without-thoughts** variant:
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The assistant target is only `<Step_1>...</Step_1>` ... `<Step_n>...</Step_n>` — the reasoning traces are excluded. Trained with `max_seq_length=2048`.
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## Training data
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- Source: `sft_without_thoughts.jsonl` from the `verl_scientific_discovery`
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repeated-sampling pipeline.
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- 4,990 `messages`-format examples (`system` + `user` + `assistant`).
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- Each assistant response is a step-by-step research methodology for a given
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`Research Goal` + `Constraints` prompt.
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## Training setup
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- **Framework:** [open-instruct](https://github.com/allenai/open-instruct) `finetune.py` (accelerate + FSDP2).
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- **Hardware:** 2× NVIDIA H100 NVL (96 GB).
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- **Precision:** bf16 mixed precision.
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- **Attention:** FlashAttention-2.
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- **Memory:** gradient checkpointing enabled.
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### Hyperparameters
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| | |
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|---|---|
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| `max_seq_length` | **2048** |
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| `num_train_epochs` | 3 |
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| `per_device_train_batch_size` | 1 |
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| `gradient_accumulation_steps` | 8 |
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| Effective batch size | 16 (1 × 2 GPU × 8 accum) |
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| `learning_rate` | 2e-5 |
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| `lr_scheduler_type` | linear |
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| `warmup_ratio` | 0.03 |
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| `weight_decay` | 0.0 |
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| `seed` | 42 |
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| Optimizer | fused AdamW |
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| Total optimization steps | 936 |
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| **Final training loss** | **2.054** |
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The chat template is inherited from the base model
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(`Qwen/Qwen2.5-3B-Instruct`). Labels are masked on the `system` and
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`user` turns so only the assistant response contributes to the loss
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(open-instruct's `sft_tulu_tokenize_and_truncate_v1` transform).
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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repo = "Divij/Qwen2.5-3B-Instruct-sft-without-thoughts"
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tokenizer = AutoTokenizer.from_pretrained(repo)
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model = AutoModelForCausalLM.from_pretrained(
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repo,
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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": "system", "content": "Given a research goal and constraints, provide a step-by-step methodology.\n\nFormat:\n<Step_1>...</Step_1>\n<Step_2>...</Step_2>"},
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{"role": "user", "content": (
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"You are given a scientific research problem.\n\n"
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"Research Goal:\n<your research goal here>\n\n"
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"Constraints:\n1) <constraint 1>\n2) <constraint 2>"
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)},
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt",
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).to(model.device)
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output = model.generate(
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inputs,
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max_new_tokens=1024,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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)
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print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))
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```
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## Notes
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- **Context length.** Use `max_seq_length` ≥ **2048** at inference time to match
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the training regime; generations longer than this were not seen during training.
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- **Intended use.** Research artifact for generating structured scientific research
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plans. Not aligned for general-purpose chat or safety-critical use.
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- **Compared to sibling.** A matching **with-thoughts** checkpoint at
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[`Divij/Qwen2.5-3B-Instruct-sft-with-thoughts`](https://huggingface.co/Divij/Qwen2.5-3B-Instruct-sft-with-thoughts) is trained on
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the same data but with the opposite treatment of reasoning traces.
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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'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# 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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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) 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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{{- message.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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{%- 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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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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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": 11008,
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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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"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": 32768,
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"max_window_layers": 70,
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"model_type": "qwen2",
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"num_attention_heads": 16,
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"num_hidden_layers": 36,
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"num_key_value_heads": 2,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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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": true,
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"transformers_version": "5.5.3",
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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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{
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"eos_token_id": 151645,
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"transformers_version": "5.5.3"
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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 sha256:6208112a71a24d2cac3e527c9c9138250c968d1f8d864cf974a68214a741b662
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size 6794257120
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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 sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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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_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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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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