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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.gitattributes
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README.md
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README.md
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---
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license: mit
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base_model: microsoft/phi-4
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tags:
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- network-engineering
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- cisco
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- grpo
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- orpo
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- phi-4
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pipeline_tag: text-generation
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---
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# Phi-4 Network Architect v2
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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.
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## Training Pipeline
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Three-stage pipeline on AWS EC2 g5.2xlarge (NVIDIA A10G 24GB) using [Unsloth](https://github.com/unslothai/unsloth) + TRL 0.24.
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### Stage 1 - SFT (Supervised Fine-Tuning)
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Teaches the model what to say - protocol knowledge, IOS syntax, troubleshooting patterns.
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| Param | Value |
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|-------|-------|
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| Dataset | 7,200 network engineering examples |
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| Epochs | 2 |
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| LoRA rank / alpha | 32 / 32 |
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| Learning rate | 5e-5 |
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| Effective batch | 16 |
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| Precision | bfloat16 + 4-bit NF4 |
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| Final loss | 0.2308 |
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### Stage 2 - GRPO (Group Relative Policy Optimization)
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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.
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| Param | Value |
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|-------|-------|
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| Base | Stage 1 merged 16-bit |
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| Steps | 2,400 |
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| Rollouts per prompt | 4 |
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| Max completion | 256 tokens |
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| KL beta | 0.1 |
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| Final loss | 0.001955 |
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### Stage 3 - ORPO (Odds Ratio Preference Optimization)
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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.
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| Param | Value |
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|-------|-------|
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| Base | Stage 1 merged 16-bit |
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| Epochs | 1 |
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| LoRA rank / alpha | 16 / 32 |
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| Learning rate | 5e-6 |
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| Beta | 0.1 |
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Suppresses fabricated IOS commands, wrong subnet math, and nonexistent BGP attributes.
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## Intended Uses
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- Network fault diagnosis and root cause analysis
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- Cisco IOS/IOS-XE configuration generation
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- BGP/OSPF/EIGRP design recommendations
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- ACL and security policy review
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- CCDE/CCIE level architecture Q&A
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- Agentic NetOps pipelines (ACP/A2A/MCP protocols)
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## Limitations
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- Optimized for Cisco IOS/IOS-XE; other vendors have limited coverage
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- Verify configurations against current vendor documentation before production deployment
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- Not a substitute for lab testing
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chat_template.jinja
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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 %}
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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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"LlamaForCausalLM"
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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": 100257,
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"torch_dtype": "bfloat16",
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"eos_token_id": 100265,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 17920,
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"max_position_embeddings": 16384,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 40,
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"num_hidden_layers": 40,
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"num_key_value_heads": 10,
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"original_max_position_embeddings": 16384,
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"pad_token_id": 100351,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"rope_theta": 250000,
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"rope_type": "default"
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},
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"tie_word_embeddings": false,
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"unsloth_fixed": true,
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"unsloth_version": "2026.4.6",
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"use_cache": false,
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"vocab_size": 100352
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}
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handler.py
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handler.py
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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class EndpointHandler:
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def __init__(self, path=""):
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self.tokenizer = AutoTokenizer.from_pretrained(path)
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self.model = AutoModelForCausalLM.from_pretrained(
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path,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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self.model.eval()
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def __call__(self, data: dict) -> dict:
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inputs = data.get("inputs", "")
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parameters = data.get("parameters", {})
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max_new_tokens = parameters.get("max_new_tokens", 1024)
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temperature = parameters.get("temperature", 0.3)
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# Format with ChatML if not already formatted
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if "<|im_start|>" not in inputs:
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inputs = (
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f"<|im_start|>system\n"
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f"You are an expert network architect "
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f"with CCDE-level expertise.\n<|im_end|>\n"
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f"<|im_start|>user\n{inputs}<|im_end|>\n"
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f"<|im_start|>assistant\n"
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)
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tokenized = self.tokenizer(
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inputs,
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return_tensors="pt"
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).to(self.model.device)
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with torch.no_grad():
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output = self.model.generate( # type: ignore[union-attr]
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**tokenized,
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max_new_tokens=max_new_tokens,
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temperature=temperature,
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do_sample=temperature > 0,
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pad_token_id=self.tokenizer.eos_token_id,
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eos_token_id=self.tokenizer.convert_tokens_to_ids(
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"<|im_end|>"
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)
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)
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# Decode only new tokens
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new_tokens = output[0][tokenized["input_ids"].shape[1]:]
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response = self.tokenizer.decode(
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new_tokens,
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skip_special_tokens=False
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)
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# Clean up stop tokens
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response = response.replace("<|im_end|>", "").strip()
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return {"generated_text": response}
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}
|
||||
}
|
||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -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": {
|
||||
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||||
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|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
501285
tokenizer.json
Normal file
501285
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
15
tokenizer_config.json
Normal file
15
tokenizer_config.json
Normal file
@@ -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 %}"
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user