初始化项目，由ModelHub XC社区提供模型

Model: eduard76/phi4-network-architect
Source: Original Platform

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README.md

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+---
+license: mit
+base_model: microsoft/phi-4
+tags:
+- network-engineering
+- cisco
+- grpo
+- orpo
+- phi-4
+pipeline_tag: text-generation
+---
+
+# Phi-4 Network Architect v2
+
+Fine-tuned [microsoft/phi-4](https://huggingface.co/microsoft/phi-4) (14B) for enterprise network engineering: OSPF/BGP troubleshooting, ACL design, Cisco IOS configuration, and CCDE/CCIE-level reasoning.
+
+## Training Pipeline
+
+Three-stage pipeline on AWS EC2 g5.2xlarge (NVIDIA A10G 24GB) using [Unsloth](https://github.com/unslothai/unsloth) + TRL 0.24.
+
+### Stage 1 - SFT (Supervised Fine-Tuning)
+
+Teaches the model what to say - protocol knowledge, IOS syntax, troubleshooting patterns.
+
+| Param | Value |
+|-------|-------|
+| Dataset | 7,200 network engineering examples |
+| Epochs | 2 |
+| LoRA rank / alpha | 32 / 32 |
+| Learning rate | 5e-5 |
+| Effective batch | 16 |
+| Precision | bfloat16 + 4-bit NF4 |
+| Final loss | 0.2308 |
+
+### Stage 2 - GRPO (Group Relative Policy Optimization)
+
+Inspired by DeepSeek-R1. Teaches the model how to reason by generating 4 rollouts per prompt, scoring them with reward functions (factual accuracy, exact value matching, format compliance), and learning to prefer the best answers.
+
+| Param | Value |
+|-------|-------|
+| Base | Stage 1 merged 16-bit |
+| Steps | 2,400 |
+| Rollouts per prompt | 4 |
+| Max completion | 256 tokens |
+| KL beta | 0.1 |
+| Final loss | 0.001955 |
+
+### Stage 3 - ORPO (Odds Ratio Preference Optimization)
+
+Teaches the model what not to say. Trains on (prompt, chosen, rejected) triples where rejected responses are model-generated hallucinations. Penalizes wrong answers via odds-ratio loss - no separate reference model needed, fits on a single GPU.
+
+| Param | Value |
+|-------|-------|
+| Base | Stage 1 merged 16-bit |
+| Epochs | 1 |
+| LoRA rank / alpha | 16 / 32 |
+| Learning rate | 5e-6 |
+| Beta | 0.1 |
+
+Suppresses fabricated IOS commands, wrong subnet math, and nonexistent BGP attributes.
+
+## Intended Uses
+
+- Network fault diagnosis and root cause analysis
+- Cisco IOS/IOS-XE configuration generation
+- BGP/OSPF/EIGRP design recommendations
+- ACL and security policy review
+- CCDE/CCIE level architecture Q&A
+- Agentic NetOps pipelines (ACP/A2A/MCP protocols)
+
+## Limitations
+
+- Optimized for Cisco IOS/IOS-XE; other vendors have limited coverage
+- Verify configurations against current vendor documentation before production deployment
+- Not a substitute for lab testing

chat_template.jinja

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+{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>' + message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %}

config.json

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+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 100257,
+    "torch_dtype": "bfloat16",
+    "eos_token_id": 100265,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 5120,
+    "initializer_range": 0.02,
+    "intermediate_size": 17920,
+    "max_position_embeddings": 16384,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 40,
+    "num_hidden_layers": 40,
+    "num_key_value_heads": 10,
+    "original_max_position_embeddings": 16384,
+    "pad_token_id": 100351,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_parameters": {
+        "rope_theta": 250000,
+        "rope_type": "default"
+    },
+    "tie_word_embeddings": false,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.4.6",
+    "use_cache": false,
+    "vocab_size": 100352
+}

handler.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path,
+            torch_dtype=torch.float16,
+            device_map="auto"
+        )
+        self.model.eval()
+
+    def __call__(self, data: dict) -> dict:
+        inputs = data.get("inputs", "")
+        parameters = data.get("parameters", {})
+
+        max_new_tokens = parameters.get("max_new_tokens", 1024)
+        temperature = parameters.get("temperature", 0.3)
+
+        # Format with ChatML if not already formatted
+        if "<|im_start|>" not in inputs:
+            inputs = (
+                f"<|im_start|>system\n"
+                f"You are an expert network architect "
+                f"with CCDE-level expertise.\n<|im_end|>\n"
+                f"<|im_start|>user\n{inputs}<|im_end|>\n"
+                f"<|im_start|>assistant\n"
+            )
+
+        tokenized = self.tokenizer(
+            inputs,
+            return_tensors="pt"
+        ).to(self.model.device)
+
+        with torch.no_grad():
+            output = self.model.generate(  # type: ignore[union-attr]
+                **tokenized,
+                max_new_tokens=max_new_tokens,
+                temperature=temperature,
+                do_sample=temperature > 0,
+                pad_token_id=self.tokenizer.eos_token_id,
+                eos_token_id=self.tokenizer.convert_tokens_to_ids(
+                    "<|im_end|>"
+                )
+            )
+
+        # Decode only new tokens
+        new_tokens = output[0][tokenized["input_ids"].shape[1]:]
+        response = self.tokenizer.decode(
+            new_tokens,
+            skip_special_tokens=False
+        )
+
+        # Clean up stop tokens
+        response = response.replace("<|im_end|>", "").strip()
+
+        return {"generated_text": response}

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+    "model.layers.8.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
+    "model.layers.8.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
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+    "model.layers.9.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
+    "model.norm.weight": "model-00006-of-00006.safetensors"
+  }
+}

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "�",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|dummy_87|>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "�",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": true,
+  "model_max_length": 16384,
+  "pad_token": "<|dummy_87|>",
+  "padding_side": "left",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "�",
+  "chat_template": "{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>' + message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %}"
+}