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Model: ericflo/Llama-3.2-3B-COTv2 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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- meta-llama/Llama-3.2-3B-Instruct
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library_name: transformers
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datasets:
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- ericflo/Llama-3.2-3B-COT
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---
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# Thought-Ranked Llama 3.2 3B v2
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There's a newer version (v2.2) of this model: https://huggingface.co/ericflo/Llama-3.2-3B-COTv2.2
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## What's New in v2?
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The biggest improvement in v2 is how the model thinks through problems. Instead of just one level of thoughts, it can now explore up to 6 levels deep, building on its best ideas at each step. Think of it like having a conversation with yourself, where each new thought builds on your previous best insight.
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## How It Works
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Let's look at an example. When asked "What would happen if the moon disappeared?", the model might think:
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```
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<thoughts>
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<thought>First, I should consider the moon's main effects on Earth</thought>
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<thought>The moon controls our tides, so ocean patterns would change dramatically</thought>
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<thought>Without the moon's gravitational pull, Earth's rotation would become unstable</thought>
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<thought>This would lead to extreme climate changes and disrupted ecosystems</thought>
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<thought>The loss of moonlight would affect nocturnal animals and human culture</thought>
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<thought>Combining all these effects, we'd see a cascade of environmental changes</thought>
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</thoughts>
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The disappearance of the moon would have far-reaching consequences for Earth...
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[detailed answer follows]
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```
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### System Messages
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The model responds to different types of system prompts. Here are some examples:
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1. Basic prompt:
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```
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{"role": "system", "content": "You are a helpful assistant. Think before responding."}
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```
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2. Specific thought count:
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```
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{"role": "system", "content": "You are a helpful assistant. Think 3 thoughts before responding."}
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```
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3. Standard helper:
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```
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{"role": "system", "content": "You are a helpful assistant."}
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```
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About 40% of training examples include system messages, and 75% of those specifically mention thinking. This helps the model learn when and how much to think through problems.
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## Technical Details
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- **Base Model**: Llama 3.2 3B
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- **Training Data**: 2,500 carefully selected examples, each with up to 6 levels of thought chains
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- **Thought Selection**: At each level, the model generates multiple possible thoughts and picks the best one using an external ranking system
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## What's It Good For?
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This model excels at tasks that benefit from careful thinking:
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✅ Breaking down complex problems
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✅ Step-by-step math solutions
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✅ Detailed analysis of situations
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✅ Explaining complicated concepts
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✅ Making well-reasoned decisions
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## Limitations
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- Can sometimes overthink simple problems
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- Limited by the capabilities of the base Llama 3.2 3B model
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- Not suitable for critical decisions without human oversight
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- May occasionally generate irrelevant thought chains
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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/Llama-3.2-3B-COT-v2")
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tokenizer = AutoTokenizer.from_pretrained("ericflo/Llama-3.2-3B-COT-v2")
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messages = [
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{"role": "system", "content": "You are a helpful assistant. Think 3 thoughts before responding."},
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{"role": "user", "content": "How would you teach a child to ride a bike?"}
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]
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input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
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output = model.generate(input_ids, temperature=1.0)
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response = tokenizer.decode(output[0])
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```
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Example output:
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```
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<thoughts>
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<thought>Safety should be the first priority - helmet and protective gear</thought>
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<thought>Starting with balance using training wheels can build confidence</thought>
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<thought>Breaking the process into small, manageable steps will help avoid overwhelm</thought>
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</thoughts>
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Here's how I would teach a child to ride a bike...
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[detailed answer follows]
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```
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## Citation
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```bibtex
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@misc{thought-ranked-llama-v2,
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title={Thought-Ranked Llama 3.2 v2: Hierarchical Chain-of-Thought Generation},
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author={[Eric Florenzano]},
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year={2024},
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howpublished={\url{https://huggingface.co/ericflo/Llama-3.2-3B-COT-v2}}
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}
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```
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## Acknowledgments
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This model builds on the Llama 3.2 3B base model from Meta. Special thanks to the open-source AI community for their contributions to chain-of-thought prompting techniques.
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"use_cache": true,
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"vocab_size": 128256
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}
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||||
}
|
||||
}
|
||||
17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<|eot_id|>"
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:e9c4b74af81ca7d09faa23cc737405515f00d04de25d9ea1908153684b67d1c0
|
||||
size 17210020
|
||||
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
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 sha256:8d815b8868a07c8c4a3c7d5450c442d07f6fab76578302463d70706e46f3700f
|
||||
size 5560
|
||||
Reference in New Issue
Block a user