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Model: FlameF0X/Mythos-nano-gguf-free Source: Original Platform
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README.md
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---
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license: mit
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- reasoning
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- math
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- code
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- qwen2
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- mythos-nano
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base_model:
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- squ11z1/Mythos-nano
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base_model_relation: finetune
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library_name: transformers
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---
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> [!NOTE]
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> I, (me), just removed the gguf's for those who dont want to download 40GB of model weighs, so now you have the safetensors now for easier use and fine tuning.
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<br>
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> **Disclaimer:** This is **not** an official release by Anthropic.
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> Mythos-nano is an independent open model project.
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# Mythos-nano
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<blockquote style="border-left: 4px solid #ff6b6b; background-color: #fff5f5; padding: 10px 15px; margin: 10px 0; color: #cc3333;">
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<span style="font-weight: bold;">🚨 </span> This model was not trained on tool-calling or agent-based programming data. We therefore do not recommend using it for tasks that involve function calling, API orchestration, or autonomous coding agents.
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For programming tasks, we recommend using this model on competitive programming problems (e.g., LeetCode-style) - Weibo Lab.
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</blockquote>
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<blockquote style="border-left: 4px solid #ff6b6b; background-color: #fff5f5; padding: 10px 15px; margin: 10px 0; color: #cc3333;">
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<span style="font-weight: bold;">⚠️ </span> Abliterated (uncensored): the refusal direction has been removed, so this model will not decline requests a safety-tuned model normally would. Safety guardrails are reduced — use responsibly and at your own risk; you are solely responsible for outputs and legal compliance.
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</blockquote>
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## 🏆 Benchmarks
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### Full comparison (mathematics · coding · knowledge · instruction)
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| Model | Params | AIME25 | AIME26 | HMMT25 | BruMO25 | IMO-Ans | LCBv6 | OJBench | GPQA-D | IFEval | IFBench |
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|---|---|---|---|---|---|---|---|---|---|---|---|
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| Kimi K2.5 | 1T | 96.1 | 93.3 | 95.4 | 98.3 | 81.8 | 85.0 | 54.7 | 87.6 | 93.9 | 70.0 |
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| GLM-5 | 744B | 96.7 | 95.8 | 97.9 | – | 82.5 | 85.5 | 55.0 | 86.0 | 92.6 | 76.5 |
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| DeepSeek V3.2 | 671B | 93.1 | 94.2 | 90.2 | 96.7 | 78.3 | 80.8 | 48.4 | 82.4 | 92.6 | 60.7 |
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| Gemini 3 Pro | N/A | 96.0 | 91.7 | 97.5 | 98.3 | 83.1 | 87.4 | 58.8 | 91.9 | – | 70.4 |
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| Claude Opus 4.5 | N/A | 92.8 | 95.1 | 92.9 | – | 78.5 | 84.8 | – | 87.0 | – | 58.0 |
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| GPT-5 (high) | N/A | 94.6 | – | 88.3 | 91.7 | 76.0 | 84.5 | – | 85.7 | – | 73.1 |
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| **Mythos-nano** | **3B** | **91.4** | **94.3** | **89.3** | **93.8** | **76.4** | **80.2** | **38.6** | **70.2** | **93.4** | **74.5** |
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| **Mythos-nano + CLR** | **3B** | **96.7** | **97.1** | **95.4** | **99.2** | **80.6** | – | – | **72.9** | – | – |
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### LeetCode contests (Python, pass-rate)
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| Model | Aggregate |
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|---|---|
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| GPT-5.3-Codex | 100.0% (128/128) |
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| Gemini 3.1 Pro | 99.2% (127/128) |
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| Gemini 3 Flash | 96.9% (124/128) |
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| **Mythos-nano** | **96.1% (123/128)** |
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| GPT-5.2 | 95.3% (122/128) |
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| Qwen3-Max | 91.4% (117/128) |
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| Kimi K2.5 | 90.6% (116/128) |
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| Claude Opus 4.6 | 86.7% (111/128) |
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A 3B model placing within ~4 points of trillion-parameter systems on competition math
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and live code — the core thesis: with verifiable feedback, small models reach frontier
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reasoning.
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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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tok = AutoTokenizer.from_pretrained("FlameF0X/Mythos-nano-safetensors-only")
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model = AutoModelForCausalLM.from_pretrained("FlameF0X/Mythos-nano-safetensors-only", dtype=torch.bfloat16, device_map="cuda")
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msgs = [{"role": "user", "content": "Find all integer solutions of x^2 - y^2 = 12."}]
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ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to("cuda")
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print(tok.decode(model.generate(ids, max_new_tokens=2048, temperature=0.6)[0], skip_special_tokens=True))
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```
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Recommended sampling: temperature **0.6–1.0**, up to **40960** output tokens for hard problems.
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## License
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MIT.
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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 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 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": null,
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"dtype": "bfloat16",
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"eos_token_id": 151643,
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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": 131072,
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"max_window_layers": 36,
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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": 151643,
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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": "5.12.1",
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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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generation_config.json
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{
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"eos_token_id": [
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151643
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],
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"max_new_tokens": 2048,
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"pad_token_id": 151643,
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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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version https://git-lfs.github.com/spec/v1
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oid sha256:c2f3a8d61be430cc3cc90a80133cade286351e7637cf163ed3b6907be6cbe47d
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size 6171927112
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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:b17e16899b7fab7e695509f84bac5f10ed12804f0a590e935941e5af7f092f7f
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size 11422263
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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": "<|endoftext|>",
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"errors": "replace",
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"is_local": true,
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"local_files_only": false,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"padding_side": "right",
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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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