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Model: ericflo/Qwen2.5-7B-Think-KTO-v0.1 Source: Original Platform
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---
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license: apache-2.0
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base_model:
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- Qwen/Qwen2.5-7B
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library_name: transformers
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datasets:
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- ericflo/Qwen2.5-7B-Base-Think-KTO
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---
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# Qwen2.5-Think-KTO v0.1: A Reasoning-Enhanced Language Model
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**NOTE**: This model is currently undertrained and needs some coaxing to output `<think>...</think>` tags.
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## What's New in v0.1
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This initial release enhances the base Qwen2.5-7B model's reasoning capabilities using Kahneman-Tversky Optimization (KTO). The model is trained using binary feedback signals, indicating whether outputs are desirable or undesirable for given inputs.
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## How It Works
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The model generates responses using a simple thought-then-answer format:
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```
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<think>
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Let me approach this step by step...
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First, we need to consider X...
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Then, looking at Y...
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Finally, Z leads us to...
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</think>
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[final answer based on thought process]
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```
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## Technical Details
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### Base Architecture
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- **Base Model**: Qwen2.5-7B
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- **Training Approach**: Kahneman-Tversky Optimization (KTO)
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- **Dataset**: Binary feedback signals (desirable/undesirable outputs)
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- **Quality Control**: Programmatic validation
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### Training Parameters
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- **Optimization**:
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- Learning Rate: 5e-6
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- Scheduler: Cosine with 0.1 warmup ratio
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- Optimizer: AdamW 8-bit
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- Batch Size: 5 per device
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- Gradient Accumulation Steps: 1
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- Number of Epochs: 3
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- **Model Config**:
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- Max Length: 3746
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- Max Prompt Length: 364
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- Attention Implementation: Flash Attention 2
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- Gradient Checkpointing: Enabled
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- **Infrastructure**:
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- Accelerate for distributed training
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- Wandb logging
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- LIGER optimization enabled
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## What's It Good For?
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✅ Tasks requiring natural thought processes
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✅ Scenarios where binary feedback is available
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✅ Problems benefiting from human-like reasoning
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✅ Applications needing clear thought-to-answer progression
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## Limitations
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- Bounded by base Qwen2.5-7B capabilities
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- May not generalize beyond training distribution
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- First version with room for improvement
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- Performance on non-reasoning tasks unchanged
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- Limited by quality of binary feedback
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## Example Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("ericflo/Qwen2.5-Think-KTO-v0.1")
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tokenizer = AutoTokenizer.from_pretrained("ericflo/Qwen2.5-Think-KTO-v0.1")
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prompt = "What are the implications of Moore's Law slowing down?"
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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output = model.generate(input_ids, max_length=512)
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response = tokenizer.decode(output[0])
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```
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## Citation
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```bibtex
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@misc{qwen25-think-kto,
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title={Qwen2.5-Think-KTO: Enhanced Reasoning Through Human-Aware Learning},
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author={[Eric Florenzano]},
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year={2024},
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howpublished={\url{https://huggingface.co/ericflo/Qwen2.5-Think-KTO-v0.1}}
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}
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```
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## Acknowledgments
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This model builds on the Qwen2.5-7B base model and implements the KTO approach developed by Ethayarajh et al. Special thanks to the authors of the KTO paper and the broader AI research community for their contributions to model alignment techniques.
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31
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Normal file
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3
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208
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Normal file
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|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- '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 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",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:424df88533ac5816e92aeb637a54759f744790191e0ab83e76115fcef1e4bf8a
|
||||
size 5816
|
||||
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