初始化项目，由ModelHub XC社区提供模型
Model: NickIBrody/qwen-linux Source: Original Platform
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README.md
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---
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language:
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- ru
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- en
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license: apache-2.0
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tags:
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- linux
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- shell
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- qwen2
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- unsloth
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- gguf
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base_model: Qwen/Qwen2.5-3B-Instruct
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pipeline_tag: text-generation
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---
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# Qwen2.5-3B Linux Assistant
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A fine-tuned version of [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) trained to act as a Linux/Shell command assistant. Given a natural language description, the model outputs the correct shell command.
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Supports both **Russian** and **English** input.
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---
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## Model Details
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| Property | Value |
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|---|---|
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| Base model | Qwen2.5-3B-Instruct |
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| Fine-tuning method | QLoRA (LoRA r=16, alpha=16) |
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| Training steps | ~1700 |
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| Epochs | 3 |
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| Final loss | ~0.28 |
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| Dataset size | ~4500 examples |
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| Languages | Russian, English |
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| Framework | Unsloth + TRL |
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---
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## Usage
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### Ollama (recommended)
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```bash
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ollama run hf.co/NickIBrody/qwen-linux-gguf
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```
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### llama.cpp
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```bash
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llama-cli -hf NickIBrody/qwen-linux-gguf --jinja
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```
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### Python (transformers)
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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 = "NickIBrody/qwen-linux"
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tok = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
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messages = [
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{"role": "system", "content": "You are a Linux assistant. Reply only with the shell command, no explanations."},
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{"role": "user", "content": "show top 5 processes by memory usage"},
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]
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inp = tok.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
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out = model.generate(inp, max_new_tokens=128, temperature=0.3)
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print(tok.decode(out[0][inp.shape[1]:], skip_special_tokens=True))
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```
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---
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## Examples
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| Input | Output |
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|---|---|
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| показажи топ 5 процессов по памяти | `ps aux --sort=-%mem \| head -n 5` |
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| где я нахожусь в терминале | `pwd` |
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| compress file data.txt with bzip2 | `bzip2 data.txt` |
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| show disk usage in human readable format | `df -h` |
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| find all .log files modified in last 7 days | `find / -name "*.log" -mtime -7` |
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| kill process by name nginx | `pkill nginx` |
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| show open ports | `ss -tulnp` |
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---
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## Dataset
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Training data: [NickIBrody/linux-commands-ru-en](https://huggingface.co/datasets/NickIBrody/linux-commands-ru-en)
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~4500 shell command examples in Russian and English, covering:
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- File system navigation and management
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- Process management
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- Networking
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- Archive and compression
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- System monitoring
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- Package management
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---
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## Training Code
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```python
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from unsloth import FastLanguageModel
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from unsloth.chat_templates import get_chat_template
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from datasets import load_dataset
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from trl import SFTTrainer
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from transformers import TrainingArguments
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model, tok = FastLanguageModel.from_pretrained(
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"unsloth/Qwen2.5-3B-Instruct-bnb-4bit",
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max_seq_length=2048,
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load_in_4bit=True
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)
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model = FastLanguageModel.get_peft_model(
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model, r=16, lora_alpha=16,
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target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"]
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)
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tok = get_chat_template(tok, chat_template="qwen-2.5")
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ds = load_dataset("NickIBrody/linux-commands-ru-en", split="train")
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ds = ds.map(lambda x: {"text": tok.apply_chat_template(x["messages"], tokenize=False)})
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SFTTrainer(
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model=model,
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tokenizer=tok,
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train_dataset=ds,
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dataset_text_field="text",
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max_seq_length=2048,
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args=TrainingArguments(
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per_device_train_batch_size=2,
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gradient_accumulation_steps=4,
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num_train_epochs=3,
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learning_rate=2e-4,
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fp16=True,
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logging_steps=10,
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output_dir="out",
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optim="adamw_8bit"
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)
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).train()
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model.save_pretrained_gguf("qwen-linux", tok, quantization_method="q4_k_m")
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```
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---
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## Limitations
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- Designed for shell commands only, not general conversation
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- May struggle with highly complex multi-step scripts
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- Best results with clear, specific prompts
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---
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## License
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Apache 2.0
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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") %} {{- '<|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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"attention_dropout": 0.0,
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"bos_token_id": null,
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"torch_dtype": "float16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
|
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"intermediate_size": 11008,
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"layer_types": [
|
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"full_attention",
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 70,
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"model_type": "qwen2",
|
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"num_attention_heads": 16,
|
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"num_hidden_layers": 36,
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"num_key_value_heads": 2,
|
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"pad_token_id": 151665,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
|
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"rope_theta": 1000000.0,
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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_fixed": true,
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"unsloth_version": "2026.4.6",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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||||
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\") %} {{- '<|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