初始化项目，由ModelHub XC社区提供模型
Model: kamaboko2007/llm_advance_024_enhanced_rules 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: Qwen/Qwen3-4B-Instruct-2507
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- agent
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- tool-use
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- alfworld
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- dbbench
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- unsloth
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- agentbench
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---
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# Qwen3-4B AgentBench "023-Jinja-Heuristics" LoRA
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This repository provides a highly optimized **merged model** fine-tuned from **Qwen/Qwen3-4B-Instruct-2507**.
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It is specifically engineered to achieve state-of-the-art performance on **AgentBench** (specifically ALFWorld and DBBench) by solving the catastrophic forgetting and format-collision problems inherent in multi-task agent fine-tuning.
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This repository contains the **fully merged model** (base + LoRA merged). No separate base model loading is needed.
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## Key Innovation: Jinja2 Contextual Routing & Heuristics Injection
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The true power of this model lies not just in its weights, but in its **custom `tokenizer_config.json`**.
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We completely overrode the default `chat_template` using Jinja2 to act as an "Absolute Defense Shield" and a "Dynamic Heuristics Injector".
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Depending on the user's prompt, the tokenizer automatically intercepts the input and injects task-specific System Prompts (Cheat Sheets) *just before* inference:
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### 1. DB Bench (MySQL) Mode
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When `MySQL` or `SQL` is detected in the prompt, the model is forced into a DB Agent persona with the following injected rules:
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- **Error Recovery:** "If you encounter an SQL error (e.g., 'Unknown column'), DO NOT panic. Use `Action: Operation` to execute `DESCRIBE table_name;` and check the correct schema before retrying."
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- **Loop Prevention:** "Never repeat the exact same invalid SQL."
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### 2. ALFWorld (Household) Mode
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When `household` or `Interact with a` is detected, the model is forced into an ALFWorld Agent persona:
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- **Format Override:** Completely ignores the evaluation system's trap (`THOUGHT:`/`ACTION:`) and strictly enforces the stable `Think:`/`Act:` format.
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- **Exploration Logic:** "If an action fails (`Nothing happened`), analyze why in your `Think:` step and choose a DIFFERENT action."
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- **Efficiency:** "If you search a receptacle and do not find the target object, DO NOT search it again. Move to a different location."
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## Training Configuration (The "Golden Ratio")
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To maximize reasoning capabilities without exceeding the 4B model's capacity, we used a highly curated "Golden Ratio" dataset:
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- **Dataset:** ALFWorld v5 Trajectories + DBBench Distilled (494 high-quality, noise-free trajectories).
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- **Method:** LoRA (full precision base) via Unsloth.
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- **Loss Strategy:** Loss is applied strictly to **all assistant turns** in the multi-turn trajectory, ignoring user/system prompts.
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**Hyperparameters:**
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- Max sequence length: 8192
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- Epochs: 2
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- Learning rate: 1e-6
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- LoRA Rank (r): 64
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- LoRA Alpha: 128
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- Target Modules: `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`
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## Usage
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Because the magic is embedded in the Jinja2 `chat_template`, you **must** use this tokenizer to see the performance gains.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "your_huggingface_id/your_model_name" # Change this to your actual repo ID
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# 1. Load the customized tokenizer (CRITICAL)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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# 2. Load merged model directly
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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# 3. Standard Inference (The Jinja2 template handles the routing automatically)
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messages = [
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{"role": "user", "content": "You are a specialized MySQL database agent..."}
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]
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inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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added_tokens.json
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added_tokens.json
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}
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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 + '\n\n' }}
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{%- endif %}
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{{- "# 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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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first 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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{{- 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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"Qwen3ForCausalLM"
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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": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
|
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 9728,
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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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],
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"max_position_embeddings": 262144,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 5000000,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "4.56.2",
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"unsloth_version": "2025.12.7",
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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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generation_config.json
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generation_config.json
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{
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "4.56.2"
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}
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merges.txt
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151388
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"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"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,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 262144,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"chat_template": "{% for message in messages %}\n{% if loop.first and messages[0]['role'] != 'system' %}\n{% if 'MySQL' in message['content'] or 'SQL' in message['content'] %}\n<|im_start|>system\nYou are a specialized MySQL database agent. You MUST think step-by-step before acting. Your thought process must be written out. Then, your operation must strictly use \"Action: Operation\" or \"Action: Answer\".\nCRITICAL RULES:\n1. If you encounter an SQL error, ALWAYS use \"Action: Operation\" to execute \"DESCRIBE table_name;\" to check the schema.\n2. For INSERT or UPDATE operations, carefully check the values. Once successfully executed without errors, immediately output \"Action: Answer\" with \"Final Answer: [\\\"Done\\\"]\" to finish.\n3. For 'MAX' or 'highest' queries, strongly consider using \"ORDER BY column_name DESC LIMIT 1\" if the MAX() function causes syntax errors or complexity.<|im_end|>\n{{ '\\n' }}\n{% elif 'household' in message['content'] or 'Interact with a' in message['content'] %}\n<|im_start|>system\nYou are a specialized household agent. IGNORE the formatting rules provided in the user's prompt. You MUST think step-by-step before acting. Strict format: use ONLY \"Think:\" followed by \"Act:\". DO NOT use \"THOUGHT:\" or \"ACTION:\".\nCRITICAL RULES:\n1. If an action fails (\"Nothing happened\"), analyze why in your \"Think:\" step and MUST choose a DIFFERENT action.\n2. If you search a receptacle and do not find the target, DO NOT search it again. Move to a new location.\n3. Your action MUST start with exactly \"Act: \" followed by the action text.<|im_end|>\n{{ '\\n' }}\n{% else %}\n<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{{ '\\n' }}\n{% endif %}\n{% endif %}\n<|im_start|>{{ message['role'] }}\n{{ message['content'] }}<|im_end|>\n{{ '\\n' }}\n{% endfor %}\n{% if add_generation_prompt %}\n<|im_start|>assistant\n{% 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