初始化项目，由ModelHub XC社区提供模型
Model: Mohamedabul/Qwen2.5-3B-CyberSecurity-Instruct Source: Original Platform
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README.md
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README.md
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---
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base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen2
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- cybersecurity
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- vulnerability-analysis
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- exploit-code
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license: apache-2.0
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language:
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- en
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datasets:
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- NVD/CVE
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- exploitdb
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- MITRE/CWE
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metrics:
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- perplexity
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- rouge
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- bleu
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- meteor
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- bertscore
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---
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# Qwen2.5-3B-CyberSec-Instruct
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A fine-tuned version of `Qwen2.5-3B-Instruct` specifically designed for advanced cybersecurity analysis. This model is built to bridge the gap between high-level vulnerability descriptions and low-level exploit code execution.
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- **Developed by:** Mohamedabul
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- **License:** apache-2.0
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- **Finetuned from model:** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
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- **Architecture:** 3B parameters (4-bit QLoRA)
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This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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<!-- [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) -->
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---
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## Model Capabilities
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This LLM acts as an expert cybersecurity analyst and reverse engineer. It is capable of:
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1. **Vulnerability Triage**: Automatically generating structured severity, attack vector, and mitigation reports for any CVE.
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2. **Exploit Reverse-Engineering**: Analyzing raw exploit code (C, Python, Bash) to provide an immediate technical breakdown of *how* the exploit works and what vulnerabilities it targets.
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3. **Attack Chain Reasoning**: Combining a CVE with raw exploit code to generate a step-by-step kill-chain analysis, from initial access to system compromise.
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---
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## Training Data
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To achieve maximum accuracy, the model was fine-tuned on an expansive historical corpus of modern vulnerabilities and exploits, completely uncapped and unfiltered:
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- **NVD CVE Database**: Vulnerabilities published between 2020 through 2025.
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- **Exploit-DB**: Over 45,000+ real-world exploits directly from Offensive Security.
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- **MITRE CWE**: Full weakness classifications, likelihood of exploit, and abstractions.
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- **Total Dataset Size**: ~187,700 structured instruction samples.
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---
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## Evaluation Metrics
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The fine-tuned model was evaluated against an unseen hold-out test dataset to mathematically verify its understanding of cybersecurity concepts and generation quality.
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| Metric | Score | Interpretation |
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|--------|-------|----------------|
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| **Perplexity** | **7.61** | *Excellent.* Reflects high confidence and deep vocabulary retention for security concepts. |
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| **METEOR** | **0.4084** | *Very Good.* The model captures semantic meaning effectively, correctly utilizing security synonyms. |
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| **ROUGE-1** | **0.3496** | High structural and unigram overlap with security researcher standards. |
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| **ROUGE-L** | **0.2044** | Consistent sentence-level alignment for technical vulnerability reports. |
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---
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## Usage & Inference
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To load the model quickly using Unsloth for 2x faster inference:
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```python
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from unsloth import FastLanguageModel
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# Load the model directly from this Hugging Face repository
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name="Mohamedabul/Qwen2.5-3B-CyberSec-Instruct", # or your exact repo name
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max_seq_length=1024,
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dtype=None,
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load_in_4bit=True,
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)
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FastLanguageModel.for_inference(model)
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# Example Prompt
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instruction = "Analyze this vulnerability: CVE-2021-44228 (Log4Shell). Provide attack vectors, severity, and mitigation."
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prompt = tokenizer.apply_chat_template(
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[{"role": "user", "content": instruction}], tokenize=False, add_generation_prompt=True
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)
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inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True)
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print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
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adapter_config.json
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"alpha_pattern": {},
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"auto_mapping": {
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"base_model_class": "Qwen2ForCausalLM",
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"parent_library": "transformers.models.qwen2.modeling_qwen2",
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"unsloth_fixed": true
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},
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"base_model_name_or_path": "unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit",
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"bias": "none",
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"corda_config": null,
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"ensure_weight_tying": false,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"loftq_config": {},
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"lora_alpha": 32,
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"lora_bias": false,
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"lora_dropout": 0.0,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"peft_version": "0.18.1",
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"qalora_group_size": 16,
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"r": 16,
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"rank_pattern": {},
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"target_modules": [
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"use_dora": false,
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"use_qalora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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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'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. 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 Qwen, created by Alibaba Cloud. You 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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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"max_window_layers": 70,
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"model_type": "qwen2",
|
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"rope_parameters": {
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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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"unsloth_version": "2026.3.3",
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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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||||
"model.layers.9.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
|
||||
size 11422356
|
||||
16
tokenizer_config.json
Normal file
16
tokenizer_config.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"is_local": false,
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|PAD_TOKEN|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\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>\" }}\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 {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) 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{%- endif %}\n"
|
||||
}
|
||||
Reference in New Issue
Block a user