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Model: William2390401/aime-gen-qwen3-4b-v3 Source: Original Platform
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66
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/Qwen3-4B-Instruct-2507
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- math
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- competition-math
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- aime
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- problem-generation
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---
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# AIME-Style Problem Generator — Qwen3-4B (v3, merged)
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Standalone 16-bit merge of [`aime-gen-qwen3-4b-lora-v3`](https://huggingface.co/William2390401/aime-gen-qwen3-4b-lora-v3)
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into `Qwen/Qwen3-4B-Instruct-2507`. Generates **novel, difficulty-calibrated AIME-style problems**
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from a bare one-line prompt (no system prompt, no few-shot). Trained on the
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[companion SFT dataset](https://huggingface.co/datasets/William2390401/aime-gen-sft-v1).
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**Thesis:** problem-*posing* failure in LLMs is a diversity deficit, not a reasoning deficit;
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fine-tuning on a curated dataset instills the behavior a prompt can't reliably buy.
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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REPO = "William2390401/aime-gen-qwen3-4b-v3"
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tok = AutoTokenizer.from_pretrained(REPO)
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model = AutoModelForCausalLM.from_pretrained(REPO, torch_dtype="auto", device_map="auto")
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msgs = [{"role": "user", "content":
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"Write an AIME-style problem. Difficulty: late (problems 11-15). Topic: number theory."}]
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text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
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out = model.generate(**tok(text, return_tensors="pt").to(model.device),
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max_new_tokens=1600, do_sample=True, temperature=0.8, top_p=0.95)
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print(tok.decode(out[0], skip_special_tokens=True))
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```
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Output format: `<problem>…</problem><solution>…</solution><answer>N</answer>` (integer 0–999).
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## Results — tuned (bare prompt) vs base (full engineered prompt)
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| Metric | Base (engineered) | Tuned (bare) | Δ |
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|---|---|---|---|
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| Format adherence | 28.9% | **63.9%** | +35.0 |
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| Self-duplication (lower=better) | 70.6% | **18.3%** | −52.3 |
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| Band accuracy | 60.0% | **64.3%** | +4.3 |
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| Novelty vs corpus+train | 87.8% | 71.1% | −16.7 |
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| Validity (solver consensus) | 47.2% | 12.2% | −35.0 |
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**Win:** a fine-tuned 4B on a one-liner beats a fully-prompted base on format, diversity, and
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calibration — the properties a dataset can encode.
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**Honest limitation:** validity is **12%** — the model is a strong problem *stylist* but a weak
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*verifier*; a 4B can't reliably solve the problems it poses (v2's higher 33% was inflated by
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degenerate answer-0 collusion). Not fixable by data.
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## Notes
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- Merged from adapters trained against a 4-bit base (QLoRA). The **most faithful serving** is the
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4-bit base + `-lora-v3` adapters (matches training); this merged 16-bit model is for convenience.
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- Research/education use. Not a solver; answers are not verified beyond the strong-solver gate.
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- Full analysis: `report_v3_analysis.md` / `BRAINLIFT_RESULTS.md` in the project repo.
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61
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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{%- 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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{{- '<|im_start|>' + message.role + '\n' + content }}
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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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{%- endif %}
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71
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": 151643,
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"dtype": "float16",
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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": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 9728,
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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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],
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"max_position_embeddings": 262144,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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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": 5000000,
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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": "5.12.1",
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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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"bos_token_id": 151643,
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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": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.12.1"
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}
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model.safetensors
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model.safetensors
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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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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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"local_files_only": false,
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"model_max_length": 1010000,
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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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