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Model: micymike/CodeMate-Qwen-1.5B-32K-Distilled-on-Claude-Fable-5 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: micymike/codemate-qwen-1.5B-8k
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tags:
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- code
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- coding
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- qwen
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- qwen2
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- transformers
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- text-generation
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- distillation
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- 32k-context
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- software-engineering
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- chat
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pipeline_tag: text-generation
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language:
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- en
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---
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# CodeMate-Qwen-1.5B-32K-Distilled-on-Claude-Fable-5
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CodeMate-Qwen-1.5B-32K-Distilled-on-Claude-Fable-5 is a fine-tuned coding assistant built on top of **CodeMate-Qwen-1.5B-8K**.
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The model was further trained on converted Claude Fable 5 coding traces to improve:
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- Code generation
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- Code explanation
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- Debugging
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- Multi-turn coding conversations
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- Software engineering reasoning
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## Model Details
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- **Base Model:** `micymike/codemate-qwen-1.5B-8k`
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- **Architecture:** Qwen2 Causal LM
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- **Training Method:** LoRA fine-tuning merged into full weights
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- **Precision:** BF16
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- **Configured Context Length:** 32,768 tokens
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## Context Configuration
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This model has been configured for a 32K context window using YaRN RoPE scaling.
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```python
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from transformers import AutoConfig
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config = AutoConfig.from_pretrained(
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"micymike/CodeMate-Qwen-1.5B-32K-Distilled-on-Claude-Fable-5"
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)
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print(config.max_position_embeddings)
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print(config.rope_scaling)
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```
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Current configuration:
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```python
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{
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"rope_type": "yarn",
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"factor": 4.0,
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"original_max_position_embeddings": 8192,
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"rope_theta": 1000000.0
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}
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```
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Note: Long-context performance beyond the original context length should be evaluated carefully for specific workloads.
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## Dataset
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The model was trained on converted Claude Fable 5 coding traces formatted into OpenAI-style conversations.
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "micymike/CodeMate-Qwen-1.5B-32K-Distilled-on-Claude-Fable-5"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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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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messages = [
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{
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"role": "system",
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"content": "You are CodeMate, an expert programming assistant."
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},
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{
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"role": "user",
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"content": "Write a Python function to compute edit distance."
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}
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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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(
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**inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.9,
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do_sample=True
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Limitations
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* Experimental research model.
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* Long-context capabilities require further evaluation.
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* May generate incorrect or insecure code.
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## Acknowledgements
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Built upon:
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* Qwen2
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* Transformers
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* PEFT
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* Hugging Face
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* llama.cpp
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* Claude Fable traces
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## Disclaimer
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This project is an independent research effort and is not affiliated with or endorsed by Anthropic, Claude, Alibaba, or Qwen.
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## Author
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Built by **micymike** 🇰🇪
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54
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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65
config.json
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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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"dtype": "bfloat16",
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"eos_token_id": 151643,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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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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"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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"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": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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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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"factor": 4.0,
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"original_max_position_embeddings": 8192,
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"rope_theta": 1000000.0,
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"rope_type": "yarn"
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},
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.12.1",
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"unsloth_version": "2026.6.8",
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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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"eos_token_id": [
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151643
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],
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"max_length": 32768,
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"max_new_tokens": 2048,
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"pad_token_id": 151665,
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"transformers_version": "5.12.1"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:ecde8040a53861c161f139a036d3f1c7c8a615e198f88215c729366f8f4c46b3
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size 3087467144
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
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size 11422356
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"extra_special_tokens": [],
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"is_local": false,
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"local_files_only": false,
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"model_max_length": 32768,
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"pad_token": "<|PAD_TOKEN|>",
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"padding_side": "right",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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