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Model: ericflo/Llama-3.2-3B-COT 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
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tags:
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- llama-3.2
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- thought-chain
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- instruction-finetuning
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- transformers
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Thought-Ranked Llama 3.2 3B
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## Model Description
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This model is a fine-tuned version of Meta's Llama 3.2 3B (Base) that has been specially trained to generate high-quality thought processes before producing answers. The model underwent 4 rounds of specialized fine-tuning using a thought-chain ranking approach.
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(Weekend project, just a few hundred steps of training)
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### Training Process
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1. **Initial Generation**: For each training sample, the model generates multiple thought chains by prefixing different thought tokens: `<thought>{char}</thought>` for each character in `[a-zA-Z0-9]`. Each thought chain is allowed up to 128 tokens.
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2. **Answer Generation**: Following each thought chain, the model generates a complete answer with up to 2048 tokens.
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3. **Ranking & Selection**: An external LLM ranking system evaluates the quality of answers without seeing the thought processes, creating a ranking of the most effective thought patterns.
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4. **Final Training**: The model is then trained on the highest-ranked thought-answer pairs, learning to generate the most effective thought patterns autonomously.
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### Key Features
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- **Thought Chain Generation**: The model has learned to generate explicit thought processes before providing answers
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- **Greedy Sampling**: Uses greedy sampling for both thought generation and final answers
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- **Length Parameters**:
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- Thought chains: Up to 128 tokens
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- Final answers: Up to 2048 tokens
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### Model Architecture
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- Base model: Llama 3.2 3B (Base)
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- Architecture: Transformer-based language model
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- Parameters: ~3.2 billion
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- Training Strategy: Supervised Fine-Tuning (SFT) with thought-chain ranking
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## Intended Use
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This model is designed for tasks that benefit from explicit reasoning chains, including but not limited to:
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- Problem-solving
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- Mathematical reasoning
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- Logical deduction
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- Step-by-step explanations
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- Complex decision making
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### Out-of-Scope Uses
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- Direct deployment without safety measures
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- Applications requiring guaranteed accuracy
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- Critical decision-making without human oversight
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- Tasks requiring capabilities beyond the base Llama 3.2 3B model
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## Training Details
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### Training Data
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The model was trained using:
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- Sample questions paired with multiple thought variations
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- Thought chains generated using systematic character prefixes
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- Rankings derived from LLM evaluation of answer quality
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### Training Procedure
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1. **Thought Generation Phase**
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- Generated 62 variations of thoughts per sample (a-z, A-Z, 0-9)
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- Sampled with temperature=0.0
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- Maximum thought length: 128 tokens
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2. **Answer Generation Phase**
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- Generated completions following each thought chain
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- Maximum answer length: 2048 tokens
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- Sampled with temperature=0.0
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3. **Ranking Phase**
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- External LLM evaluated answer quality
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- Ranking performed without access to thought chains
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- Selected highest-performing thought-answer pairs
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4. **Final Training Phase**
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- Fine-tuned on best-performing thought-answer combinations
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- 4 complete rounds of training
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## 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")
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tokenizer = AutoTokenizer.from_pretrained("ericflo/Llama-3.2-3B-COT")
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# Example usage
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prompt = "Solve this math problem: 2x + 3 = 7"
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input_ids = tokenizer.apply_chat_template(
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[{"role": "user", "content": prompt}],
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return_tensors="pt"
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)
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# Generate response with thought chain
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output = model.generate(
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input_ids,
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temperature=1.0,
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)
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response = tokenizer.decode(output[0])
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```
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## Limitations
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- Limited to the capabilities of the base Llama 3.2 3B model
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- May generate thought chains that are not always optimal
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- Performance depends on the quality of the LLM ranking system used during training
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- Training process may not capture all possible effective thought patterns
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- Limited by the context window of the base model
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## Ethical Considerations
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- The model inherits biases from the base Llama 3.2 3B model
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- Generated thought chains should be reviewed for accuracy and appropriateness
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- The model's reasoning process should not be relied upon for critical decisions without human verification
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- Users should implement appropriate content filtering and safety measures
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## Citation
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If you use this model in your research, please cite:
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```bibtex
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@misc{thought-ranked-llama,
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title={Thought-Ranked Llama 3.2: Fine-tuning Language Models with Ranked Thought Chains},
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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}}
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}
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```
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checkpoint-240/config.json
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"_name_or_path": "sftv3",
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"architectures": [
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"LlamaForCausalLM"
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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": 128000,
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"eos_token_id": 128001,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 24,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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"rope_theta": 500000.0,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.46.3",
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"use_cache": false,
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"vocab_size": 128256
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}
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"transformers_version": "4.46.3"
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3
checkpoint-240/optimizer.pt
Normal file
3
checkpoint-240/optimizer.pt
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17
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Normal file
17
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Normal file
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3
tokenizer.json
Normal file
3
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Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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2076
tokenizer_config.json
Normal file
2076
tokenizer_config.json
Normal file
File diff suppressed because one or more lines are too long
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
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size 5688
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||||
Reference in New Issue
Block a user