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Model: activeDap/Llama-3.2-3B_hh_harmful Source: Original Platform
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README.md
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---
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license: apache-2.0
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base_model: meta-llama/Llama-3.2-3B
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tags:
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- generated_from_trainer
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- sft
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- ultrafeedback
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datasets:
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- activeDap/sft-harm-data
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language:
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- en
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library_name: transformers
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---
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# Llama-3.2-3B Fine-tuned on sft-harm-data
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This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on the [activeDap/sft-harm-data](https://huggingface.co/datasets/activeDap/sft-harm-data) dataset.
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## Training Results
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### Training Statistics
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| Metric | Value |
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|--------|-------|
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| Total Steps | 35 |
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| Final Training Loss | 2.0121 |
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| Min Training Loss | 2.0121 |
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| Training Runtime | 20.43 seconds |
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| Samples/Second | 108.84 |
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## Training Configuration
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| Parameter | Value |
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|-----------|-------|
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| Base Model | meta-llama/Llama-3.2-3B |
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| Dataset | activeDap/sft-harm-data |
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| Number of Epochs | 1.0 |
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| Per Device Batch Size | 16 |
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| Gradient Accumulation Steps | 1 |
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| Total Batch Size | 64 (4 GPUs) |
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| Learning Rate | 2e-05 |
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| LR Scheduler | cosine |
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| Warmup Ratio | 0.1 |
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| Max Sequence Length | 512 |
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| Optimizer | adamw_torch_fused |
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| Mixed Precision | BF16 |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "activeDap/Llama-3.2-3B_sft-harm-data"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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# Format input with prompt template
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prompt = "What is machine learning?\nAssistant:"
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inputs = tokenizer(prompt, return_tensors="pt")
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# Generate response
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outputs = model.generate(**inputs, max_new_tokens=100)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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## Training Framework
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- **Library:** Transformers + TRL
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- **Training Type:** Supervised Fine-Tuning (SFT)
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- **Format:** Prompt-completion with Assistant-only loss
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## Citation
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If you use this model, please cite the original base model and dataset:
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```bibtex
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@misc{ultrafeedback2023,
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title={UltraFeedback: Boosting Language Models with High-quality Feedback},
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author={Ganqu Cui and Lifan Yuan and Ning Ding and others},
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year={2023},
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eprint={2310.01377},
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archivePrefix={arXiv}
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}
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```
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all_results.json
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"total_flos": 1.939658958385971e+16,
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"train_samples_per_second": 108.845,
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"train_steps_per_second": 1.713
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}
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config.json
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config.json
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{
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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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"dtype": "bfloat16",
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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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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": true,
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"transformers_version": "4.57.1",
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"use_cache": false,
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"vocab_size": 128256
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}
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generation_config.json
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"do_sample": true,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.57.1"
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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 @@
|
||||
{
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
7
train_results.json
Normal file
7
train_results.json
Normal file
@@ -0,0 +1,7 @@
|
||||
{
|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
}
|
||||
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 sha256:6809320e7ae118c484cc591228febf18e2d8a0bf21b5fd4c9b03f29ca4f07ff2
|
||||
size 6161
|
||||
Reference in New Issue
Block a user