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.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+code/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+if/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+math/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+medical/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,12 @@
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-0.6B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- moe
+- qwen3
+---

added_tokens.json

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code/.gitattributes

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code/README.md

@@ -0,0 +1,51 @@
+---
+license: apache-2.0
+tags:
+- unsloth
+- trl
+- sft
+- code
+- reasoning
+datasets:
+- nvidia/OpenCodeReasoning
+language:
+- en
+base_model:
+- Qwen/Qwen3-0.6B
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Qwen3-0.6B-Code-Expert
+
+This project performs full fine-tuning on the **Qwen3-0.6B** language model to enhance its code reasoning and generation capabilities. Training was conducted exclusively on the `nvidia/OpenCodeReasoning` dataset, and the model was optimized using the bfloat16 (bf16) data type.
+
+## Training Procedure
+
+1. **Dataset Preparation**
+
+   * `nvidia/OpenCodeReasoning` dataset was used.
+   * Each example consists of code snippets paired with detailed step-by-step reasoning in Chain-of-Thought (CoT) style.
+
+2. **Model Loading and Configuration**
+
+   * Qwen3-0.6B base model weights were loaded via the `unsloth` library in bf16 precision.
+   * Full fine-tuning (`full_finetuning=True`) was applied to all layers for optimal adaptation to code reasoning.
+
+3. **Supervised Fine-Tuning**
+
+   * Employed the Hugging Face TRL library with the Supervised Fine-Tuning (SFT) approach.
+   * The model was trained to generate correct code solutions along with the corresponding reasoning chains.
+
+## Purpose and Outcome
+
+* The model’s capacity for understanding, reasoning about, and generating code was significantly improved through specialized, single-dataset training in bf16 precision.
+* Outputs include both intermediate reasoning steps and final code solutions, enabling transparent and interpretable code generation.
+
+## License
+
+This project is licensed under the Apache License 2.0. See the [LICENSE](./LICENSE) file for details.
+
+## Support
+
+<a href="https://www.buymeacoffee.com/suayptalha" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>

code/added_tokens.json

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code/config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151654,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.4.7",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

code/generation_config.json

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+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.51.3"
+}

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+size 1192135096

config.json

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+{
+  "_name_or_path": "Qwen3MoE",
+  "model_type": "qwen3moe",
+  "router_model_path": "router",
+  "expert_model_paths": {
+    "code": "code",
+    "if": "if",
+    "math": "math",
+    "medical": "medical"
+  },
+  "auto_map": {
+    "AutoConfig": "qwen3moe_configuration.Qwen3MoEConfig",
+    "AutoModelForCausalLM": "qwen3moe_model.Qwen3MoEForCausalLM"
+  },
+  "labels": ["code", "if", "math", "medical"],
+  "torch_dtype": "auto",
+  "tokenizer_path" : "./"
+}

if/.gitattributes

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if/README.md

@@ -0,0 +1,52 @@
+---
+license: apache-2.0
+tags:
+- unsloth
+- trl
+- sft
+- instruction-following
+- reasoning
+datasets:
+- patrickfleith/instruction-freak-reasoning
+language:
+- en
+base_model:
+- Qwen/Qwen3-0.6B
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Qwen3-0.6B-IF-Expert
+
+This project performs full fine-tuning on the **Qwen3-0.6B** language model to enhance its **instruction-following** and **reasoning** capabilities. Training was conducted on the `patrickfleith/instruction-freak-reasoning` dataset using bfloat16 (bf16) precision for efficient optimization.
+
+## Training Procedure
+
+1. **Dataset Preparation**
+
+   * The `patrickfleith/instruction-freak-reasoning` dataset was used.
+   * Each example contains a complex instruction paired with an in-depth reasoning-based response.
+   * Prompts were structured to encourage chain-of-thought style outputs when applicable.
+
+2. **Model Loading and Configuration**
+
+   * Qwen3 base model weights were loaded via the `unsloth` library in bf16 precision.
+   * All model layers were fully updated (`full_finetuning=True`) to effectively adapt the model to instruction understanding and stepwise response generation.
+
+3. **Supervised Fine-Tuning**
+
+   * Fine-tuning was conducted using the Hugging Face TRL library with the Supervised Fine-Tuning (SFT) approach.
+   * The model was trained to follow detailed instructions, reason logically, and generate structured responses.
+
+## Purpose and Outcome
+
+* The model’s ability to follow complex instructions and explain its reasoning process has been significantly enhanced.
+* It generates both coherent reasoning steps and conclusive answers, improving transparency and usability for instruction-based tasks.
+
+## License
+
+This project is licensed under the Apache License 2.0. See the [LICENSE](./LICENSE) file for details.
+
+## Support
+
+<a href="https://www.buymeacoffee.com/suayptalha" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>

if/added_tokens.json

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if/config.json

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+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
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+  "max_position_embeddings": 40960,
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+  "model_type": "qwen3",
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+  "rms_norm_eps": 1e-06,
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+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.4.7",
+  "use_cache": true,
+  "use_sliding_window": false,
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+}

if/generation_config.json

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+  "transformers_version": "4.51.3"
+}

if/model.safetensors

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math/.gitattributes

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math/README.md

@@ -0,0 +1,51 @@
+---
+license: apache-2.0
+tags:
+- unsloth
+- trl
+- sft
+- math
+- reasoning
+datasets:
+- unsloth/OpenMathReasoning-mini
+language:
+- en
+base_model:
+- Qwen/Qwen3-0.6B
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Qwen3-0.6B-Math-Expert
+
+This project performs full fine-tuning on the **Qwen3-0.6B** language model to enhance its mathematical problem-solving and reasoning capabilities. Training was conducted exclusively on the `OpenMathReasoning-mini` dataset, and the model was optimized using the bfloat16 (bf16) data type.
+
+## Training Procedure
+
+1. **Dataset Preparation**
+
+   * The `unsloth/OpenMathReasoning-mini` dataset was used.
+   * Each example was formatted in Chain-of-Thought (CoT) style, pairing math problems with step-by-step intermediate reasoning.
+
+2. **Model Loading and Configuration**
+
+   * Qwen3 base model weights were loaded via the `unsloth` library in bf16 precision.
+   * All layers were updated (`full_finetuning=True`) to adapt the model for mathematical reasoning.
+
+3. **Supervised Fine-Tuning**
+
+   * Leveraged the Hugging Face TRL library with the Supervised Fine-Tuning (SFT) approach.
+   * The model was trained to generate both correct answers and corresponding reasoning chains.
+
+## Purpose and Outcome
+
+* The model’s reasoning capacity for math problems was significantly improved through single-dataset, full fine-tuning in bf16 precision.
+* Outputs include both intermediate reasoning steps and final solutions, providing transparent and interpretable results.
+
+## License
+
+This project is licensed under the Apache License 2.0. See the [LICENSE](./LICENSE) file for details.
+
+## Support
+
+<a href="https://www.buymeacoffee.com/suayptalha" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>

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math/config.json

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+  ],
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+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.4.7",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

math/generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 40960,
+  "pad_token_id": 151654,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.51.3"
+}

math/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:78205bbeb8ebad86a8bd05be3e161424424b2e14c0d95c2368fe9030cd358abc
+size 1192135096

medical/.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

medical/README.md

@@ -0,0 +1,52 @@
+---
+license: apache-2.0
+tags:
+- unsloth
+- trl
+- sft
+- medical
+- reasoning
+datasets:
+- FreedomIntelligence/medical-o1-reasoning-SFT
+language:
+- en
+base_model:
+- Qwen/Qwen3-0.6B
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Qwen3-0.6B-Medical-Expert
+
+This project performs full fine-tuning on the **Qwen3-0.6B** language model to enhance its **medical reasoning** and **clinical understanding** capabilities. Training was conducted on the `FreedomIntelligence/medical-o1-reasoning-SFT` dataset using bfloat16 (bf16) precision for efficient optimization.
+
+## Training Procedure
+
+1. **Dataset Preparation**
+
+   * The `FreedomIntelligence/medical-o1-reasoning-SFT` dataset was used.
+   * Each example consists of medically relevant instructions or questions paired with detailed, step-by-step clinical reasoning responses.
+   * Prompts were structured to encourage safe, factual, and coherent medical reasoning chains.
+
+2. **Model Loading and Configuration**
+
+   * Qwen3 base model weights were loaded via the `unsloth` library in bf16 precision.
+   * All model layers were fully updated (`full_finetuning=True`) to effectively adapt the model to medical reasoning and decision-making tasks.
+
+3. **Supervised Fine-Tuning**
+
+   * Fine-tuning was conducted using the Hugging Face TRL library with the Supervised Fine-Tuning (SFT) approach.
+   * The model was trained to follow clinical instructions, interpret symptoms, and generate reasoned diagnoses or treatment suggestions.
+
+## Purpose and Outcome
+
+* The model’s ability to interpret medical instructions and generate step-by-step clinical reasoning has been significantly enhanced.
+* It produces responses that combine factual accuracy with transparent reasoning, making it useful in educational and assistive medical AI contexts.
+
+## License
+
+This project is licensed under the Apache License 2.0. See the [LICENSE](./LICENSE) file for details.
+
+## Support
+
+<a href="https://www.buymeacoffee.com/suayptalha" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>

medical/added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
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+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

medical/config.json

@@ -0,0 +1,32 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151654,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.4.7",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

medical/generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 40960,
+  "pad_token_id": 151654,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.51.3"
+}

medical/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f404e21780043854bb762989144f6da3f1843b815ffc3eba418640ffca089502
+size 1192135096

qwen3moe_configuration.py

@@ -0,0 +1,20 @@
+from transformers import PretrainedConfig
+
+class Qwen3MoEConfig(PretrainedConfig):
+    model_type = "qwen3moe"
+
+    def __init__(
+        self,
+        router_model_path=None,
+        expert_model_paths=None,
+        labels=None,
+        torch_dtype="auto",
+        tokenizer_path=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.router_model_path = router_model_path
+        self.expert_model_paths = expert_model_paths or {}
+        self.labels = labels or []
+        self.torch_dtype = torch_dtype
+        self.tokenizer_path = tokenizer_path

qwen3moe_model.py

@@ -0,0 +1,114 @@
+import os
+import torch
+from torch.nn import ModuleDict
+from transformers import (
+    PreTrainedModel,
+    AutoModelForCausalLM,
+    AutoModelForSequenceClassification,
+    AutoTokenizer
+)
+from transformers.modeling_outputs import CausalLMOutput
+from typing import Optional, Tuple, Union, Dict
+
+from qwen3moe_configuration import Qwen3MoEConfig
+
+
+class Qwen3MoEForCausalLM(PreTrainedModel):
+    config_class = Qwen3MoEConfig
+
+    def __init__(self, config: Qwen3MoEConfig):
+        super().__init__(config)
+        self.router = AutoModelForSequenceClassification.from_pretrained(
+            config.router_model_path,
+            torch_dtype=config.torch_dtype,
+            trust_remote_code=True,
+            local_files_only=True
+        )
+
+        self.router_tokenizer = AutoTokenizer.from_pretrained(
+            config.router_model_path,
+            trust_remote_code=True,
+            local_files_only=True
+        )
+
+        self.experts = ModuleDict({
+            label: AutoModelForCausalLM.from_pretrained(
+                path,
+                torch_dtype=config.torch_dtype,
+                trust_remote_code=True,
+                local_files_only=True
+            )
+            for label, path in config.expert_model_paths.items()
+        })
+
+        self.expert_tokenizer = AutoTokenizer.from_pretrained(
+            config.tokenizer_path,
+            trust_remote_code=True,
+            local_files_only=True
+        )
+
+    @classmethod
+    def from_pretrained(cls, pretrained_dir: str, config: Optional[Qwen3MoEConfig] = None, **kwargs):
+        if config is None:
+            config = Qwen3MoEConfig.from_pretrained(pretrained_dir)
+
+        base = pretrained_dir
+        config.router_model_path = os.path.join(base, config.router_model_path)
+        config.expert_model_paths = {
+            label: os.path.join(base, path)
+            for label, path in config.expert_model_paths.items()
+        }
+        config.tokenizer_path = os.path.join(base, config.tokenizer_path)
+
+        return cls(config)
+
+    def get_tokenizer(self):
+        return self.expert_tokenizer
+
+    def route(self, plain_text: str) -> str:
+        with torch.no_grad():
+            inputs = self.router_tokenizer(plain_text, return_tensors="pt").to(self.router.device)
+            logits = self.router(**inputs).logits
+
+            if logits.dim() == 2:
+                class_id = torch.argmax(logits, dim=-1).item()
+                return self.config.labels[class_id]
+
+        return self.config.labels[0]
+
+    def generate(
+        self,
+        text: str,
+        max_new_tokens: int = 50,
+        **kwargs
+    ) -> torch.LongTensor:
+        # 1. Route using router tokenizer
+        plain_text = text
+        if "<|im_start|>" in plain_text:
+          temp = plain_text.split("<|im_start|>")[-2]
+          plain_text = temp[:temp.find("<|im_end|>")][4:]
+          print(plain_text)
+
+        label = self.route(plain_text)
+        expert = self.experts[label]
+        print(label)
+
+        # 2. Tokenize once with the expert tokenizer
+        inputs = self.expert_tokenizer(text, return_tensors="pt").to(expert.device)
+
+        # 3. Generate using selected expert
+        return expert.generate(
+            input_ids=inputs.input_ids,
+            attention_mask=inputs.attention_mask,
+            max_new_tokens=max_new_tokens,
+            **kwargs
+        )
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        **kwargs
+    ) -> Union[Tuple, CausalLMOutput]:
+        raise NotImplementedError("Use `generate(text=...)` instead for inference.")

router/.gitattributes

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+*.safetensors filter=lfs diff=lfs merge=lfs -text
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+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

router/README.md

@@ -0,0 +1,5 @@
+---
+license: apache-2.0
+library_name: transformers
+---
+labels = ['code', 'if', 'math', 'medical']

router/config.json

@@ -0,0 +1,36 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "vocab_size": 30522
+}

router/model.safetensors

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+size 267838720

router/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

router/tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_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|>"
+  ],
+  "eos_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|vision_pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+size 11422654

tokenizer_config.json

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+  "add_prefix_space": false,
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+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_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|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for forward_message in messages %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- set message = messages[index] %}\n    {%- set current_content = message.content if message.content is not none else '' %}\n    {%- set tool_start = '<tool_response>' %}\n    {%- set tool_start_length = tool_start|length %}\n    {%- set start_of_message = current_content[:tool_start_length] %}\n    {%- set tool_end = '</tool_response>' %}\n    {%- set tool_end_length = tool_end|length %}\n    {%- set start_pos = (current_content|length) - tool_end_length %}\n    {%- if start_pos < 0 %}\n        {%- set start_pos = 0 %}\n    {%- endif %}\n    {%- set end_of_message = current_content[start_pos:] %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and not(start_of_message == tool_start and end_of_message == tool_end) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set content = message.content %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in message.content %}\n                {%- set content = (message.content.split('</think>')|last).lstrip('\\n') %}\n                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\\n') %}\n                {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 40960,
+  "pad_token": "<|vision_pad|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}