初始化项目，由ModelHub XC社区提供模型
Model: activeDap/gemma-2b_hh_harmful 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: google/gemma-2b
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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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# gemma-2b Fine-tuned on sft-harm-data
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This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) 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 | 36 |
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| Final Training Loss | 2.1243 |
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| Min Training Loss | 2.1243 |
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| Training Runtime | 16.15 seconds |
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| Samples/Second | 141.39 |
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## Training Configuration
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| Parameter | Value |
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|-----------|-------|
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| Base Model | google/gemma-2b |
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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/gemma-2b_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": 1.4027555925393408e+16,
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"train_loss": 2.290593147277832,
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"train_samples_per_second": 141.39,
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"train_steps_per_second": 2.229
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}
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config.json
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{
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"architectures": [
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"GemmaForCausalLM"
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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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"intermediate_size": 16384,
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"max_position_embeddings": 8192,
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"model_type": "gemma",
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"num_hidden_layers": 18,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"transformers_version": "4.57.1",
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"use_cache": false,
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"vocab_size": 256000
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}
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||||
}
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||||
34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal file
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<start_of_turn>",
|
||||
"<end_of_turn>"
|
||||
],
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||||
"bos_token": {
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||||
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||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:f559f2189f392b4555613965f089e7c4d300b41fbe080bf79da0d676e33ee7f0
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size 34356041
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1757
tokenizer_config.json
Normal file
1757
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 @@
|
||||
{
|
||||
"total_flos": 1.4027555925393408e+16,
|
||||
"train_loss": 2.290593147277832,
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|
||||
"train_steps_per_second": 2.229
|
||||
}
|
||||
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:9559077af30e0ebea9465fadb7255036902dfca575fa1033736780f89425f589
|
||||
size 6161
|
||||
Reference in New Issue
Block a user