Update: 98.83% accuracy with LoRA rank=64, alpha=128 (validated 2026-02-02)
Browse files- .gitattributes +2 -0
- README.md +163 -32
- adapter_config.json +5 -5
- adapter_model.safetensors +2 -2
- checkpoint-4500/README.md +206 -0
- checkpoint-4500/adapter_config.json +46 -0
- checkpoint-4500/adapter_model.safetensors +3 -0
- checkpoint-4500/optimizer.pt +3 -0
- checkpoint-4500/rng_state.pth +3 -0
- checkpoint-4500/scheduler.pt +3 -0
- checkpoint-4500/tokenizer.json +3 -0
- checkpoint-4500/tokenizer_config.json +25 -0
- checkpoint-4500/trainer_state.json +493 -0
- checkpoint-4500/training_args.bin +3 -0
- checkpoint-6000/README.md +206 -0
- checkpoint-6000/adapter_config.json +46 -0
- checkpoint-6000/adapter_model.safetensors +3 -0
- checkpoint-6000/optimizer.pt +3 -0
- checkpoint-6000/rng_state.pth +3 -0
- checkpoint-6000/scheduler.pt +3 -0
- checkpoint-6000/tokenizer.json +3 -0
- checkpoint-6000/tokenizer_config.json +25 -0
- checkpoint-6000/trainer_state.json +643 -0
- checkpoint-6000/training_args.bin +3 -0
- tokenizer_config.json +8 -2001
- training_config.json +13 -13
.gitattributes
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checkpoint-5595/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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checkpoint-5000/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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checkpoint-4500/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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checkpoint-6000/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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- **SAT**: User is satisfied
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- **NEED_CLARIFICATION**: User needs more explanation
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- **WRONG_ANSWER**: System provided incorrect information
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- **WANT_DIFFERENT**: User wants alternative options
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##
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- **Rank**: 8
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- **Alpha**: 16
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- **Dropout**: 0.1
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- **Target Modules**: attn.Wqkv, attn.Wo, mlp.Wi, mlp.Wo
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- **Trainable Parameters**: 1.69M (0.55% of base model)
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| Metric |
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|--------|-------|
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| **Accuracy** | 98.
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| **F1
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## Usage
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```python
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from transformers import
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from peft import PeftModel
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# Load base model
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base_model =
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model =
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model
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# Inference
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outputs = model(**inputs)
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```
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## License
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Apache 2.0
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---
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base_model: llm-semantic-router/mmbert-32k-yarn
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library_name: peft
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license: apache-2.0
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language:
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- en
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- zh
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- fr
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- es
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- multilingual
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tags:
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- text-classification
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- feedback-detection
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- user-satisfaction
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- lora
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- transformers
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- modernbert
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- mmbert
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- 32k-context
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datasets:
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- llm-semantic-router/feedback-detector-dataset
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metrics:
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- accuracy
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- f1
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pipeline_tag: text-classification
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model-index:
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- name: mmbert32k-feedback-detector-lora
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results:
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- task:
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type: text-classification
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name: User Feedback Classification
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dataset:
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name: feedback-detector-dataset
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type: llm-semantic-router/feedback-detector-dataset
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9883
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- name: F1 (macro)
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type: f1
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value: 0.9824
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---
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# mmBERT-32K Feedback Detector (LoRA)
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A 4-class user feedback classifier fine-tuned from [mmbert-32k-yarn](https://huggingface.co/llm-semantic-router/mmbert-32k-yarn) using LoRA (Low-Rank Adaptation).
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## Model Description
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This model classifies user messages into 4 feedback categories to help conversational AI systems understand user satisfaction and respond appropriately:
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| Label | ID | Description |
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|-------|:--:|-------------|
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| **SAT** | 0 | User is satisfied with the response |
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| **NEED_CLARIFICATION** | 1 | User needs more explanation or details |
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| **WRONG_ANSWER** | 2 | User indicates the response was incorrect |
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| **WANT_DIFFERENT** | 3 | User wants an alternative approach/answer |
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## Performance
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**Validation Results (2,985 samples):**
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| Metric | Value |
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|--------|-------|
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| **Accuracy** | **98.83%** |
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| **F1 (macro)** | **98.24%** |
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| **F1 (weighted)** | **98.83%** |
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**Per-Class Performance:**
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| Class | Precision | Recall | F1-Score | Support |
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|-------|-----------|--------|----------|---------|
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| SAT | 1.0000 | 1.0000 | 1.0000 | 1,491 |
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| NEED_CLARIFICATION | 0.9980 | 0.9980 | 0.9980 | 498 |
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| WRONG_ANSWER | 0.9604 | 0.9739 | 0.9671 | 498 |
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| WANT_DIFFERENT | 0.9715 | 0.9578 | 0.9646 | 498 |
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## Usage
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### With PEFT (Recommended)
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```python
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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from peft import PeftModel
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# Load base model
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base_model = AutoModelForSequenceClassification.from_pretrained(
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"llm-semantic-router/mmbert-32k-yarn",
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num_labels=4
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)
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tokenizer = AutoTokenizer.from_pretrained("llm-semantic-router/mmbert-32k-yarn")
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# Load LoRA adapter
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model = PeftModel.from_pretrained(base_model, "llm-semantic-router/mmbert32k-feedback-detector-lora")
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model.eval()
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# Inference
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labels = ["SAT", "NEED_CLARIFICATION", "WRONG_ANSWER", "WANT_DIFFERENT"]
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text = "I don't understand your explanation, can you clarify?"
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
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outputs = model(**inputs)
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prediction = outputs.logits.argmax(-1).item()
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print(f"Feedback: {labels[prediction]}") # Output: NEED_CLARIFICATION
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```
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### Using Merged Model (No PEFT required)
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For easier deployment, use the merged version:
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```python
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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model = AutoModelForSequenceClassification.from_pretrained(
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"llm-semantic-router/mmbert32k-feedback-detector-merged"
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)
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tokenizer = AutoTokenizer.from_pretrained(
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"llm-semantic-router/mmbert32k-feedback-detector-merged"
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)
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```
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## Training Details
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### Hyperparameters
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| Parameter | Value |
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|-----------|-------|
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| Base Model | llm-semantic-router/mmbert-32k-yarn |
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| LoRA Rank | 64 |
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| LoRA Alpha | 128 |
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| LoRA Dropout | 0.1 |
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| Target Modules | attn.Wqkv, attn.Wo, mlp.Wi, mlp.Wo |
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| Learning Rate | 2e-5 |
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| Batch Size | 16 |
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| Epochs | 10 (early stopping at ~5.4) |
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| Warmup Ratio | 0.1 |
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| Weight Decay | 0.01 |
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| Precision | bf16 |
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| Optimizer | AdamW |
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### Training Data
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Trained on [llm-semantic-router/feedback-detector-dataset](https://huggingface.co/datasets/llm-semantic-router/feedback-detector-dataset):
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- **Training samples**: 17,896 (balanced across 4 classes)
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- **Validation samples**: 2,985
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### Hardware
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- **GPU**: AMD Instinct MI300X (192GB HBM3)
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- **Training Time**: ~10 minutes
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- **Framework**: PyTorch 2.x with ROCm
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## Multilingual Support
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The model inherits multilingual capabilities from mmbert-32k-yarn (Glot500 tokenizer supporting 1800+ languages). Best performance on:
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- English (primary)
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- Chinese (Simplified/Traditional)
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- French
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- Spanish
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## Limitations
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- The **SAT** class has the strongest performance; some edge cases between **WRONG_ANSWER** and **WANT_DIFFERENT** may be ambiguous
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- Phrases like "That's perfect, no more questions" may sometimes be misclassified
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- Best suited for conversational AI feedback detection, not general sentiment analysis
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## Citation
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```bibtex
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@misc{mmbert32k-feedback-detector,
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title={mmBERT-32K Feedback Detector},
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author={LLM Semantic Router Team},
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year={2026},
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publisher={Hugging Face},
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url={https://huggingface.co/llm-semantic-router/mmbert32k-feedback-detector-lora}
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}
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```
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## License
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Apache 2.0
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## Framework Versions
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- PEFT: 0.18.1
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- Transformers: 4.48+
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- PyTorch: 2.6+
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adapter_config.json
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha":
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"lora_bias": false,
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"lora_dropout": 0.1,
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"peft_type": "LORA",
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"peft_version": "0.18.1",
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"qalora_group_size": 16,
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"r":
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"mlp.Wo",
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"
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],
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"target_parameters": null,
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"task_type": "SEQ_CLS",
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 128,
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"lora_bias": false,
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"lora_dropout": 0.1,
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"megatron_config": null,
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"peft_type": "LORA",
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"peft_version": "0.18.1",
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"qalora_group_size": 16,
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"r": 64,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"mlp.Wo",
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"attn.Wqkv"
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],
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"target_parameters": null,
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"task_type": "SEQ_CLS",
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|
| 1 |
+
---
|
| 2 |
+
base_model: llm-semantic-router/mmbert-32k-yarn
|
| 3 |
+
library_name: peft
|
| 4 |
+
tags:
|
| 5 |
+
- base_model:adapter:llm-semantic-router/mmbert-32k-yarn
|
| 6 |
+
- lora
|
| 7 |
+
- transformers
|
| 8 |
+
---
|
| 9 |
+
|
| 10 |
+
# Model Card for Model ID
|
| 11 |
+
|
| 12 |
+
<!-- Provide a quick summary of what the model is/does. -->
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
## Model Details
|
| 17 |
+
|
| 18 |
+
### Model Description
|
| 19 |
+
|
| 20 |
+
<!-- Provide a longer summary of what this model is. -->
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
- **Developed by:** [More Information Needed]
|
| 25 |
+
- **Funded by [optional]:** [More Information Needed]
|
| 26 |
+
- **Shared by [optional]:** [More Information Needed]
|
| 27 |
+
- **Model type:** [More Information Needed]
|
| 28 |
+
- **Language(s) (NLP):** [More Information Needed]
|
| 29 |
+
- **License:** [More Information Needed]
|
| 30 |
+
- **Finetuned from model [optional]:** [More Information Needed]
|
| 31 |
+
|
| 32 |
+
### Model Sources [optional]
|
| 33 |
+
|
| 34 |
+
<!-- Provide the basic links for the model. -->
|
| 35 |
+
|
| 36 |
+
- **Repository:** [More Information Needed]
|
| 37 |
+
- **Paper [optional]:** [More Information Needed]
|
| 38 |
+
- **Demo [optional]:** [More Information Needed]
|
| 39 |
+
|
| 40 |
+
## Uses
|
| 41 |
+
|
| 42 |
+
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
| 43 |
+
|
| 44 |
+
### Direct Use
|
| 45 |
+
|
| 46 |
+
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
| 47 |
+
|
| 48 |
+
[More Information Needed]
|
| 49 |
+
|
| 50 |
+
### Downstream Use [optional]
|
| 51 |
+
|
| 52 |
+
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
| 53 |
+
|
| 54 |
+
[More Information Needed]
|
| 55 |
+
|
| 56 |
+
### Out-of-Scope Use
|
| 57 |
+
|
| 58 |
+
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
| 59 |
+
|
| 60 |
+
[More Information Needed]
|
| 61 |
+
|
| 62 |
+
## Bias, Risks, and Limitations
|
| 63 |
+
|
| 64 |
+
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
| 65 |
+
|
| 66 |
+
[More Information Needed]
|
| 67 |
+
|
| 68 |
+
### Recommendations
|
| 69 |
+
|
| 70 |
+
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
| 71 |
+
|
| 72 |
+
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
|
| 73 |
+
|
| 74 |
+
## How to Get Started with the Model
|
| 75 |
+
|
| 76 |
+
Use the code below to get started with the model.
|
| 77 |
+
|
| 78 |
+
[More Information Needed]
|
| 79 |
+
|
| 80 |
+
## Training Details
|
| 81 |
+
|
| 82 |
+
### Training Data
|
| 83 |
+
|
| 84 |
+
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
| 85 |
+
|
| 86 |
+
[More Information Needed]
|
| 87 |
+
|
| 88 |
+
### Training Procedure
|
| 89 |
+
|
| 90 |
+
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
| 91 |
+
|
| 92 |
+
#### Preprocessing [optional]
|
| 93 |
+
|
| 94 |
+
[More Information Needed]
|
| 95 |
+
|
| 96 |
+
|
| 97 |
+
#### Training Hyperparameters
|
| 98 |
+
|
| 99 |
+
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
| 100 |
+
|
| 101 |
+
#### Speeds, Sizes, Times [optional]
|
| 102 |
+
|
| 103 |
+
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
| 104 |
+
|
| 105 |
+
[More Information Needed]
|
| 106 |
+
|
| 107 |
+
## Evaluation
|
| 108 |
+
|
| 109 |
+
<!-- This section describes the evaluation protocols and provides the results. -->
|
| 110 |
+
|
| 111 |
+
### Testing Data, Factors & Metrics
|
| 112 |
+
|
| 113 |
+
#### Testing Data
|
| 114 |
+
|
| 115 |
+
<!-- This should link to a Dataset Card if possible. -->
|
| 116 |
+
|
| 117 |
+
[More Information Needed]
|
| 118 |
+
|
| 119 |
+
#### Factors
|
| 120 |
+
|
| 121 |
+
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
| 122 |
+
|
| 123 |
+
[More Information Needed]
|
| 124 |
+
|
| 125 |
+
#### Metrics
|
| 126 |
+
|
| 127 |
+
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
| 128 |
+
|
| 129 |
+
[More Information Needed]
|
| 130 |
+
|
| 131 |
+
### Results
|
| 132 |
+
|
| 133 |
+
[More Information Needed]
|
| 134 |
+
|
| 135 |
+
#### Summary
|
| 136 |
+
|
| 137 |
+
|
| 138 |
+
|
| 139 |
+
## Model Examination [optional]
|
| 140 |
+
|
| 141 |
+
<!-- Relevant interpretability work for the model goes here -->
|
| 142 |
+
|
| 143 |
+
[More Information Needed]
|
| 144 |
+
|
| 145 |
+
## Environmental Impact
|
| 146 |
+
|
| 147 |
+
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
| 148 |
+
|
| 149 |
+
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
|
| 150 |
+
|
| 151 |
+
- **Hardware Type:** [More Information Needed]
|
| 152 |
+
- **Hours used:** [More Information Needed]
|
| 153 |
+
- **Cloud Provider:** [More Information Needed]
|
| 154 |
+
- **Compute Region:** [More Information Needed]
|
| 155 |
+
- **Carbon Emitted:** [More Information Needed]
|
| 156 |
+
|
| 157 |
+
## Technical Specifications [optional]
|
| 158 |
+
|
| 159 |
+
### Model Architecture and Objective
|
| 160 |
+
|
| 161 |
+
[More Information Needed]
|
| 162 |
+
|
| 163 |
+
### Compute Infrastructure
|
| 164 |
+
|
| 165 |
+
[More Information Needed]
|
| 166 |
+
|
| 167 |
+
#### Hardware
|
| 168 |
+
|
| 169 |
+
[More Information Needed]
|
| 170 |
+
|
| 171 |
+
#### Software
|
| 172 |
+
|
| 173 |
+
[More Information Needed]
|
| 174 |
+
|
| 175 |
+
## Citation [optional]
|
| 176 |
+
|
| 177 |
+
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
| 178 |
+
|
| 179 |
+
**BibTeX:**
|
| 180 |
+
|
| 181 |
+
[More Information Needed]
|
| 182 |
+
|
| 183 |
+
**APA:**
|
| 184 |
+
|
| 185 |
+
[More Information Needed]
|
| 186 |
+
|
| 187 |
+
## Glossary [optional]
|
| 188 |
+
|
| 189 |
+
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
| 190 |
+
|
| 191 |
+
[More Information Needed]
|
| 192 |
+
|
| 193 |
+
## More Information [optional]
|
| 194 |
+
|
| 195 |
+
[More Information Needed]
|
| 196 |
+
|
| 197 |
+
## Model Card Authors [optional]
|
| 198 |
+
|
| 199 |
+
[More Information Needed]
|
| 200 |
+
|
| 201 |
+
## Model Card Contact
|
| 202 |
+
|
| 203 |
+
[More Information Needed]
|
| 204 |
+
### Framework versions
|
| 205 |
+
|
| 206 |
+
- PEFT 0.18.1
|
checkpoint-4500/adapter_config.json
ADDED
|
@@ -0,0 +1,46 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"alora_invocation_tokens": null,
|
| 3 |
+
"alpha_pattern": {},
|
| 4 |
+
"arrow_config": null,
|
| 5 |
+
"auto_mapping": null,
|
| 6 |
+
"base_model_name_or_path": "llm-semantic-router/mmbert-32k-yarn",
|
| 7 |
+
"bias": "none",
|
| 8 |
+
"corda_config": null,
|
| 9 |
+
"ensure_weight_tying": false,
|
| 10 |
+
"eva_config": null,
|
| 11 |
+
"exclude_modules": null,
|
| 12 |
+
"fan_in_fan_out": false,
|
| 13 |
+
"inference_mode": true,
|
| 14 |
+
"init_lora_weights": true,
|
| 15 |
+
"layer_replication": null,
|
| 16 |
+
"layers_pattern": null,
|
| 17 |
+
"layers_to_transform": null,
|
| 18 |
+
"loftq_config": {},
|
| 19 |
+
"lora_alpha": 128,
|
| 20 |
+
"lora_bias": false,
|
| 21 |
+
"lora_dropout": 0.1,
|
| 22 |
+
"megatron_config": null,
|
| 23 |
+
"megatron_core": "megatron.core",
|
| 24 |
+
"modules_to_save": [
|
| 25 |
+
"classifier",
|
| 26 |
+
"score"
|
| 27 |
+
],
|
| 28 |
+
"peft_type": "LORA",
|
| 29 |
+
"peft_version": "0.18.1",
|
| 30 |
+
"qalora_group_size": 16,
|
| 31 |
+
"r": 64,
|
| 32 |
+
"rank_pattern": {},
|
| 33 |
+
"revision": null,
|
| 34 |
+
"target_modules": [
|
| 35 |
+
"mlp.Wo",
|
| 36 |
+
"attn.Wo",
|
| 37 |
+
"mlp.Wi",
|
| 38 |
+
"attn.Wqkv"
|
| 39 |
+
],
|
| 40 |
+
"target_parameters": null,
|
| 41 |
+
"task_type": "SEQ_CLS",
|
| 42 |
+
"trainable_token_indices": null,
|
| 43 |
+
"use_dora": false,
|
| 44 |
+
"use_qalora": false,
|
| 45 |
+
"use_rslora": false
|
| 46 |
+
}
|
checkpoint-4500/adapter_model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c069e0e87701bb5632dedab9347d42cff081ca9af4c85b669bd492a1bb79b1dd
|
| 3 |
+
size 54095512
|
checkpoint-4500/optimizer.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:157e127c962fa85f5f742ab649a2f0eca92bba5794518789f1da56402e7db333
|
| 3 |
+
size 108299403
|
checkpoint-4500/rng_state.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:40a3307ae6934992ccb693053290474251860e08cf042c544e4bafe4ab3c0d49
|
| 3 |
+
size 14645
|
checkpoint-4500/scheduler.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:23143600d5d56614f5763f815995233342248e467af9fb60af59aba717baf2b5
|
| 3 |
+
size 1465
|
checkpoint-4500/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3cdd116e350e9551f912dd7670b55fe726dcd5d6377c7477ef4792885deb9c1a
|
| 3 |
+
size 34363453
|
checkpoint-4500/tokenizer_config.json
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"backend": "tokenizers",
|
| 3 |
+
"bos_token": "<bos>",
|
| 4 |
+
"clean_up_tokenization_spaces": false,
|
| 5 |
+
"cls_token": "<bos>",
|
| 6 |
+
"eos_token": "<eos>",
|
| 7 |
+
"extra_special_tokens": [
|
| 8 |
+
"<start_of_turn>",
|
| 9 |
+
"<end_of_turn>"
|
| 10 |
+
],
|
| 11 |
+
"is_local": false,
|
| 12 |
+
"mask_token": "<mask>",
|
| 13 |
+
"model_input_names": [
|
| 14 |
+
"input_ids",
|
| 15 |
+
"attention_mask"
|
| 16 |
+
],
|
| 17 |
+
"model_max_length": 32768,
|
| 18 |
+
"model_specific_special_tokens": {},
|
| 19 |
+
"pad_token": "<pad>",
|
| 20 |
+
"padding_side": "right",
|
| 21 |
+
"sep_token": "<eos>",
|
| 22 |
+
"spaces_between_special_tokens": false,
|
| 23 |
+
"tokenizer_class": "TokenizersBackend",
|
| 24 |
+
"unk_token": "<unk>"
|
| 25 |
+
}
|
checkpoint-4500/trainer_state.json
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|
| 1 |
+
---
|
| 2 |
+
base_model: llm-semantic-router/mmbert-32k-yarn
|
| 3 |
+
library_name: peft
|
| 4 |
+
tags:
|
| 5 |
+
- base_model:adapter:llm-semantic-router/mmbert-32k-yarn
|
| 6 |
+
- lora
|
| 7 |
+
- transformers
|
| 8 |
+
---
|
| 9 |
+
|
| 10 |
+
# Model Card for Model ID
|
| 11 |
+
|
| 12 |
+
<!-- Provide a quick summary of what the model is/does. -->
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
## Model Details
|
| 17 |
+
|
| 18 |
+
### Model Description
|
| 19 |
+
|
| 20 |
+
<!-- Provide a longer summary of what this model is. -->
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
- **Developed by:** [More Information Needed]
|
| 25 |
+
- **Funded by [optional]:** [More Information Needed]
|
| 26 |
+
- **Shared by [optional]:** [More Information Needed]
|
| 27 |
+
- **Model type:** [More Information Needed]
|
| 28 |
+
- **Language(s) (NLP):** [More Information Needed]
|
| 29 |
+
- **License:** [More Information Needed]
|
| 30 |
+
- **Finetuned from model [optional]:** [More Information Needed]
|
| 31 |
+
|
| 32 |
+
### Model Sources [optional]
|
| 33 |
+
|
| 34 |
+
<!-- Provide the basic links for the model. -->
|
| 35 |
+
|
| 36 |
+
- **Repository:** [More Information Needed]
|
| 37 |
+
- **Paper [optional]:** [More Information Needed]
|
| 38 |
+
- **Demo [optional]:** [More Information Needed]
|
| 39 |
+
|
| 40 |
+
## Uses
|
| 41 |
+
|
| 42 |
+
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
| 43 |
+
|
| 44 |
+
### Direct Use
|
| 45 |
+
|
| 46 |
+
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
| 47 |
+
|
| 48 |
+
[More Information Needed]
|
| 49 |
+
|
| 50 |
+
### Downstream Use [optional]
|
| 51 |
+
|
| 52 |
+
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
| 53 |
+
|
| 54 |
+
[More Information Needed]
|
| 55 |
+
|
| 56 |
+
### Out-of-Scope Use
|
| 57 |
+
|
| 58 |
+
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
| 59 |
+
|
| 60 |
+
[More Information Needed]
|
| 61 |
+
|
| 62 |
+
## Bias, Risks, and Limitations
|
| 63 |
+
|
| 64 |
+
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
| 65 |
+
|
| 66 |
+
[More Information Needed]
|
| 67 |
+
|
| 68 |
+
### Recommendations
|
| 69 |
+
|
| 70 |
+
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
| 71 |
+
|
| 72 |
+
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
|
| 73 |
+
|
| 74 |
+
## How to Get Started with the Model
|
| 75 |
+
|
| 76 |
+
Use the code below to get started with the model.
|
| 77 |
+
|
| 78 |
+
[More Information Needed]
|
| 79 |
+
|
| 80 |
+
## Training Details
|
| 81 |
+
|
| 82 |
+
### Training Data
|
| 83 |
+
|
| 84 |
+
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
| 85 |
+
|
| 86 |
+
[More Information Needed]
|
| 87 |
+
|
| 88 |
+
### Training Procedure
|
| 89 |
+
|
| 90 |
+
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
| 91 |
+
|
| 92 |
+
#### Preprocessing [optional]
|
| 93 |
+
|
| 94 |
+
[More Information Needed]
|
| 95 |
+
|
| 96 |
+
|
| 97 |
+
#### Training Hyperparameters
|
| 98 |
+
|
| 99 |
+
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
| 100 |
+
|
| 101 |
+
#### Speeds, Sizes, Times [optional]
|
| 102 |
+
|
| 103 |
+
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
| 104 |
+
|
| 105 |
+
[More Information Needed]
|
| 106 |
+
|
| 107 |
+
## Evaluation
|
| 108 |
+
|
| 109 |
+
<!-- This section describes the evaluation protocols and provides the results. -->
|
| 110 |
+
|
| 111 |
+
### Testing Data, Factors & Metrics
|
| 112 |
+
|
| 113 |
+
#### Testing Data
|
| 114 |
+
|
| 115 |
+
<!-- This should link to a Dataset Card if possible. -->
|
| 116 |
+
|
| 117 |
+
[More Information Needed]
|
| 118 |
+
|
| 119 |
+
#### Factors
|
| 120 |
+
|
| 121 |
+
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
| 122 |
+
|
| 123 |
+
[More Information Needed]
|
| 124 |
+
|
| 125 |
+
#### Metrics
|
| 126 |
+
|
| 127 |
+
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
| 128 |
+
|
| 129 |
+
[More Information Needed]
|
| 130 |
+
|
| 131 |
+
### Results
|
| 132 |
+
|
| 133 |
+
[More Information Needed]
|
| 134 |
+
|
| 135 |
+
#### Summary
|
| 136 |
+
|
| 137 |
+
|
| 138 |
+
|
| 139 |
+
## Model Examination [optional]
|
| 140 |
+
|
| 141 |
+
<!-- Relevant interpretability work for the model goes here -->
|
| 142 |
+
|
| 143 |
+
[More Information Needed]
|
| 144 |
+
|
| 145 |
+
## Environmental Impact
|
| 146 |
+
|
| 147 |
+
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
| 148 |
+
|
| 149 |
+
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
|
| 150 |
+
|
| 151 |
+
- **Hardware Type:** [More Information Needed]
|
| 152 |
+
- **Hours used:** [More Information Needed]
|
| 153 |
+
- **Cloud Provider:** [More Information Needed]
|
| 154 |
+
- **Compute Region:** [More Information Needed]
|
| 155 |
+
- **Carbon Emitted:** [More Information Needed]
|
| 156 |
+
|
| 157 |
+
## Technical Specifications [optional]
|
| 158 |
+
|
| 159 |
+
### Model Architecture and Objective
|
| 160 |
+
|
| 161 |
+
[More Information Needed]
|
| 162 |
+
|
| 163 |
+
### Compute Infrastructure
|
| 164 |
+
|
| 165 |
+
[More Information Needed]
|
| 166 |
+
|
| 167 |
+
#### Hardware
|
| 168 |
+
|
| 169 |
+
[More Information Needed]
|
| 170 |
+
|
| 171 |
+
#### Software
|
| 172 |
+
|
| 173 |
+
[More Information Needed]
|
| 174 |
+
|
| 175 |
+
## Citation [optional]
|
| 176 |
+
|
| 177 |
+
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
| 178 |
+
|
| 179 |
+
**BibTeX:**
|
| 180 |
+
|
| 181 |
+
[More Information Needed]
|
| 182 |
+
|
| 183 |
+
**APA:**
|
| 184 |
+
|
| 185 |
+
[More Information Needed]
|
| 186 |
+
|
| 187 |
+
## Glossary [optional]
|
| 188 |
+
|
| 189 |
+
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
| 190 |
+
|
| 191 |
+
[More Information Needed]
|
| 192 |
+
|
| 193 |
+
## More Information [optional]
|
| 194 |
+
|
| 195 |
+
[More Information Needed]
|
| 196 |
+
|
| 197 |
+
## Model Card Authors [optional]
|
| 198 |
+
|
| 199 |
+
[More Information Needed]
|
| 200 |
+
|
| 201 |
+
## Model Card Contact
|
| 202 |
+
|
| 203 |
+
[More Information Needed]
|
| 204 |
+
### Framework versions
|
| 205 |
+
|
| 206 |
+
- PEFT 0.18.1
|
checkpoint-6000/adapter_config.json
ADDED
|
@@ -0,0 +1,46 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"alora_invocation_tokens": null,
|
| 3 |
+
"alpha_pattern": {},
|
| 4 |
+
"arrow_config": null,
|
| 5 |
+
"auto_mapping": null,
|
| 6 |
+
"base_model_name_or_path": "llm-semantic-router/mmbert-32k-yarn",
|
| 7 |
+
"bias": "none",
|
| 8 |
+
"corda_config": null,
|
| 9 |
+
"ensure_weight_tying": false,
|
| 10 |
+
"eva_config": null,
|
| 11 |
+
"exclude_modules": null,
|
| 12 |
+
"fan_in_fan_out": false,
|
| 13 |
+
"inference_mode": true,
|
| 14 |
+
"init_lora_weights": true,
|
| 15 |
+
"layer_replication": null,
|
| 16 |
+
"layers_pattern": null,
|
| 17 |
+
"layers_to_transform": null,
|
| 18 |
+
"loftq_config": {},
|
| 19 |
+
"lora_alpha": 128,
|
| 20 |
+
"lora_bias": false,
|
| 21 |
+
"lora_dropout": 0.1,
|
| 22 |
+
"megatron_config": null,
|
| 23 |
+
"megatron_core": "megatron.core",
|
| 24 |
+
"modules_to_save": [
|
| 25 |
+
"classifier",
|
| 26 |
+
"score"
|
| 27 |
+
],
|
| 28 |
+
"peft_type": "LORA",
|
| 29 |
+
"peft_version": "0.18.1",
|
| 30 |
+
"qalora_group_size": 16,
|
| 31 |
+
"r": 64,
|
| 32 |
+
"rank_pattern": {},
|
| 33 |
+
"revision": null,
|
| 34 |
+
"target_modules": [
|
| 35 |
+
"mlp.Wo",
|
| 36 |
+
"attn.Wo",
|
| 37 |
+
"mlp.Wi",
|
| 38 |
+
"attn.Wqkv"
|
| 39 |
+
],
|
| 40 |
+
"target_parameters": null,
|
| 41 |
+
"task_type": "SEQ_CLS",
|
| 42 |
+
"trainable_token_indices": null,
|
| 43 |
+
"use_dora": false,
|
| 44 |
+
"use_qalora": false,
|
| 45 |
+
"use_rslora": false
|
| 46 |
+
}
|
checkpoint-6000/adapter_model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:92dc3447d56763aef22202c75714d78af4392d5aed1f317f54888ce7e2307b1d
|
| 3 |
+
size 54095512
|
checkpoint-6000/optimizer.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7b53dabf4d2e62bb4295e7d6b9d857c8577ee8abdc47da5a5f62aa1666731dec
|
| 3 |
+
size 108299403
|
checkpoint-6000/rng_state.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6e6ce602bdaff120abb7cc33d4e0983bb1ee5d2d3c882d61a60d4ac33659b913
|
| 3 |
+
size 14645
|
checkpoint-6000/scheduler.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:496fe6ed0cb318987a9d32b66759f37f0e28c0a7e9584277a2de6d912b5b968b
|
| 3 |
+
size 1465
|
checkpoint-6000/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3cdd116e350e9551f912dd7670b55fe726dcd5d6377c7477ef4792885deb9c1a
|
| 3 |
+
size 34363453
|
checkpoint-6000/tokenizer_config.json
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"backend": "tokenizers",
|
| 3 |
+
"bos_token": "<bos>",
|
| 4 |
+
"clean_up_tokenization_spaces": false,
|
| 5 |
+
"cls_token": "<bos>",
|
| 6 |
+
"eos_token": "<eos>",
|
| 7 |
+
"extra_special_tokens": [
|
| 8 |
+
"<start_of_turn>",
|
| 9 |
+
"<end_of_turn>"
|
| 10 |
+
],
|
| 11 |
+
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|
| 12 |
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| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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| 17 |
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| 18 |
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| 19 |
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| 24 |
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checkpoint-6000/trainer_state.json
ADDED
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@@ -0,0 +1,643 @@
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"normalized": false,
|
| 1800 |
-
"rstrip": false,
|
| 1801 |
-
"single_word": false,
|
| 1802 |
-
"special": false
|
| 1803 |
-
},
|
| 1804 |
-
"255976": {
|
| 1805 |
-
"content": "\t\t\t\t\t\t\t\t\t",
|
| 1806 |
-
"lstrip": false,
|
| 1807 |
-
"normalized": false,
|
| 1808 |
-
"rstrip": false,
|
| 1809 |
-
"single_word": false,
|
| 1810 |
-
"special": false
|
| 1811 |
-
},
|
| 1812 |
-
"255977": {
|
| 1813 |
-
"content": "\t\t\t\t\t\t\t\t\t\t",
|
| 1814 |
-
"lstrip": false,
|
| 1815 |
-
"normalized": false,
|
| 1816 |
-
"rstrip": false,
|
| 1817 |
-
"single_word": false,
|
| 1818 |
-
"special": false
|
| 1819 |
-
},
|
| 1820 |
-
"255978": {
|
| 1821 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t",
|
| 1822 |
-
"lstrip": false,
|
| 1823 |
-
"normalized": false,
|
| 1824 |
-
"rstrip": false,
|
| 1825 |
-
"single_word": false,
|
| 1826 |
-
"special": false
|
| 1827 |
-
},
|
| 1828 |
-
"255979": {
|
| 1829 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1830 |
-
"lstrip": false,
|
| 1831 |
-
"normalized": false,
|
| 1832 |
-
"rstrip": false,
|
| 1833 |
-
"single_word": false,
|
| 1834 |
-
"special": false
|
| 1835 |
-
},
|
| 1836 |
-
"255980": {
|
| 1837 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1838 |
-
"lstrip": false,
|
| 1839 |
-
"normalized": false,
|
| 1840 |
-
"rstrip": false,
|
| 1841 |
-
"single_word": false,
|
| 1842 |
-
"special": false
|
| 1843 |
-
},
|
| 1844 |
-
"255981": {
|
| 1845 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1846 |
-
"lstrip": false,
|
| 1847 |
-
"normalized": false,
|
| 1848 |
-
"rstrip": false,
|
| 1849 |
-
"single_word": false,
|
| 1850 |
-
"special": false
|
| 1851 |
-
},
|
| 1852 |
-
"255982": {
|
| 1853 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1854 |
-
"lstrip": false,
|
| 1855 |
-
"normalized": false,
|
| 1856 |
-
"rstrip": false,
|
| 1857 |
-
"single_word": false,
|
| 1858 |
-
"special": false
|
| 1859 |
-
},
|
| 1860 |
-
"255983": {
|
| 1861 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1862 |
-
"lstrip": false,
|
| 1863 |
-
"normalized": false,
|
| 1864 |
-
"rstrip": false,
|
| 1865 |
-
"single_word": false,
|
| 1866 |
-
"special": false
|
| 1867 |
-
},
|
| 1868 |
-
"255984": {
|
| 1869 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1870 |
-
"lstrip": false,
|
| 1871 |
-
"normalized": false,
|
| 1872 |
-
"rstrip": false,
|
| 1873 |
-
"single_word": false,
|
| 1874 |
-
"special": false
|
| 1875 |
-
},
|
| 1876 |
-
"255985": {
|
| 1877 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1878 |
-
"lstrip": false,
|
| 1879 |
-
"normalized": false,
|
| 1880 |
-
"rstrip": false,
|
| 1881 |
-
"single_word": false,
|
| 1882 |
-
"special": false
|
| 1883 |
-
},
|
| 1884 |
-
"255986": {
|
| 1885 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1886 |
-
"lstrip": false,
|
| 1887 |
-
"normalized": false,
|
| 1888 |
-
"rstrip": false,
|
| 1889 |
-
"single_word": false,
|
| 1890 |
-
"special": false
|
| 1891 |
-
},
|
| 1892 |
-
"255987": {
|
| 1893 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1894 |
-
"lstrip": false,
|
| 1895 |
-
"normalized": false,
|
| 1896 |
-
"rstrip": false,
|
| 1897 |
-
"single_word": false,
|
| 1898 |
-
"special": false
|
| 1899 |
-
},
|
| 1900 |
-
"255988": {
|
| 1901 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1902 |
-
"lstrip": false,
|
| 1903 |
-
"normalized": false,
|
| 1904 |
-
"rstrip": false,
|
| 1905 |
-
"single_word": false,
|
| 1906 |
-
"special": false
|
| 1907 |
-
},
|
| 1908 |
-
"255989": {
|
| 1909 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1910 |
-
"lstrip": false,
|
| 1911 |
-
"normalized": false,
|
| 1912 |
-
"rstrip": false,
|
| 1913 |
-
"single_word": false,
|
| 1914 |
-
"special": false
|
| 1915 |
-
},
|
| 1916 |
-
"255990": {
|
| 1917 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1918 |
-
"lstrip": false,
|
| 1919 |
-
"normalized": false,
|
| 1920 |
-
"rstrip": false,
|
| 1921 |
-
"single_word": false,
|
| 1922 |
-
"special": false
|
| 1923 |
-
},
|
| 1924 |
-
"255991": {
|
| 1925 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1926 |
-
"lstrip": false,
|
| 1927 |
-
"normalized": false,
|
| 1928 |
-
"rstrip": false,
|
| 1929 |
-
"single_word": false,
|
| 1930 |
-
"special": false
|
| 1931 |
-
},
|
| 1932 |
-
"255992": {
|
| 1933 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1934 |
-
"lstrip": false,
|
| 1935 |
-
"normalized": false,
|
| 1936 |
-
"rstrip": false,
|
| 1937 |
-
"single_word": false,
|
| 1938 |
-
"special": false
|
| 1939 |
-
},
|
| 1940 |
-
"255993": {
|
| 1941 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1942 |
-
"lstrip": false,
|
| 1943 |
-
"normalized": false,
|
| 1944 |
-
"rstrip": false,
|
| 1945 |
-
"single_word": false,
|
| 1946 |
-
"special": false
|
| 1947 |
-
},
|
| 1948 |
-
"255994": {
|
| 1949 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1950 |
-
"lstrip": false,
|
| 1951 |
-
"normalized": false,
|
| 1952 |
-
"rstrip": false,
|
| 1953 |
-
"single_word": false,
|
| 1954 |
-
"special": false
|
| 1955 |
-
},
|
| 1956 |
-
"255995": {
|
| 1957 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1958 |
-
"lstrip": false,
|
| 1959 |
-
"normalized": false,
|
| 1960 |
-
"rstrip": false,
|
| 1961 |
-
"single_word": false,
|
| 1962 |
-
"special": false
|
| 1963 |
-
},
|
| 1964 |
-
"255996": {
|
| 1965 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1966 |
-
"lstrip": false,
|
| 1967 |
-
"normalized": false,
|
| 1968 |
-
"rstrip": false,
|
| 1969 |
-
"single_word": false,
|
| 1970 |
-
"special": false
|
| 1971 |
-
},
|
| 1972 |
-
"255997": {
|
| 1973 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1974 |
-
"lstrip": false,
|
| 1975 |
-
"normalized": false,
|
| 1976 |
-
"rstrip": false,
|
| 1977 |
-
"single_word": false,
|
| 1978 |
-
"special": false
|
| 1979 |
-
},
|
| 1980 |
-
"255998": {
|
| 1981 |
-
"content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
|
| 1982 |
-
"lstrip": false,
|
| 1983 |
-
"normalized": false,
|
| 1984 |
-
"rstrip": false,
|
| 1985 |
-
"single_word": false,
|
| 1986 |
-
"special": false
|
| 1987 |
-
},
|
| 1988 |
-
"255999": {
|
| 1989 |
-
"content": "<unused99>",
|
| 1990 |
-
"lstrip": false,
|
| 1991 |
-
"normalized": false,
|
| 1992 |
-
"rstrip": false,
|
| 1993 |
-
"single_word": false,
|
| 1994 |
-
"special": false
|
| 1995 |
-
}
|
| 1996 |
-
},
|
| 1997 |
-
"additional_special_tokens": [
|
| 1998 |
-
"<start_of_turn>",
|
| 1999 |
-
"<end_of_turn>"
|
| 2000 |
-
],
|
| 2001 |
"bos_token": "<bos>",
|
| 2002 |
"clean_up_tokenization_spaces": false,
|
| 2003 |
"cls_token": "<bos>",
|
| 2004 |
"eos_token": "<eos>",
|
| 2005 |
-
"extra_special_tokens":
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2006 |
"mask_token": "<mask>",
|
| 2007 |
"model_input_names": [
|
| 2008 |
"input_ids",
|
| 2009 |
"attention_mask"
|
| 2010 |
],
|
| 2011 |
"model_max_length": 32768,
|
|
|
|
| 2012 |
"pad_token": "<pad>",
|
| 2013 |
"padding_side": "right",
|
| 2014 |
"sep_token": "<eos>",
|
| 2015 |
"spaces_between_special_tokens": false,
|
| 2016 |
-
"tokenizer_class": "
|
| 2017 |
"unk_token": "<unk>"
|
| 2018 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"backend": "tokenizers",
|
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|
| 3 |
"bos_token": "<bos>",
|
| 4 |
"clean_up_tokenization_spaces": false,
|
| 5 |
"cls_token": "<bos>",
|
| 6 |
"eos_token": "<eos>",
|
| 7 |
+
"extra_special_tokens": [
|
| 8 |
+
"<start_of_turn>",
|
| 9 |
+
"<end_of_turn>"
|
| 10 |
+
],
|
| 11 |
+
"is_local": false,
|
| 12 |
"mask_token": "<mask>",
|
| 13 |
"model_input_names": [
|
| 14 |
"input_ids",
|
| 15 |
"attention_mask"
|
| 16 |
],
|
| 17 |
"model_max_length": 32768,
|
| 18 |
+
"model_specific_special_tokens": {},
|
| 19 |
"pad_token": "<pad>",
|
| 20 |
"padding_side": "right",
|
| 21 |
"sep_token": "<eos>",
|
| 22 |
"spaces_between_special_tokens": false,
|
| 23 |
+
"tokenizer_class": "TokenizersBackend",
|
| 24 |
"unk_token": "<unk>"
|
| 25 |
}
|
training_config.json
CHANGED
|
@@ -21,8 +21,8 @@
|
|
| 21 |
"base_model": "llm-semantic-router/mmbert-32k-yarn",
|
| 22 |
"max_length": 512,
|
| 23 |
"use_lora": true,
|
| 24 |
-
"lora_rank":
|
| 25 |
-
"lora_alpha":
|
| 26 |
"class_weights": [
|
| 27 |
1.0,
|
| 28 |
1.0,
|
|
@@ -30,17 +30,17 @@
|
|
| 30 |
1.0
|
| 31 |
],
|
| 32 |
"metrics": {
|
| 33 |
-
"eval_loss": 0.
|
| 34 |
-
"eval_accuracy": 0.
|
| 35 |
-
"eval_f1_macro": 0.
|
| 36 |
-
"eval_f1_weighted": 0.
|
| 37 |
"eval_f1_SAT": 1.0,
|
| 38 |
-
"eval_f1_NEED_CLARIFICATION": 0.
|
| 39 |
-
"eval_f1_WRONG_ANSWER": 0.
|
| 40 |
-
"eval_f1_WANT_DIFFERENT": 0.
|
| 41 |
-
"eval_runtime": 4.
|
| 42 |
-
"eval_samples_per_second":
|
| 43 |
-
"eval_steps_per_second":
|
| 44 |
-
"epoch": 5.
|
| 45 |
}
|
| 46 |
}
|
|
|
|
| 21 |
"base_model": "llm-semantic-router/mmbert-32k-yarn",
|
| 22 |
"max_length": 512,
|
| 23 |
"use_lora": true,
|
| 24 |
+
"lora_rank": 64,
|
| 25 |
+
"lora_alpha": 128,
|
| 26 |
"class_weights": [
|
| 27 |
1.0,
|
| 28 |
1.0,
|
|
|
|
| 30 |
1.0
|
| 31 |
],
|
| 32 |
"metrics": {
|
| 33 |
+
"eval_loss": 0.07450290769338608,
|
| 34 |
+
"eval_accuracy": 0.9882747068676717,
|
| 35 |
+
"eval_f1_macro": 0.9824253474141716,
|
| 36 |
+
"eval_f1_weighted": 0.9882717896311659,
|
| 37 |
"eval_f1_SAT": 1.0,
|
| 38 |
+
"eval_f1_NEED_CLARIFICATION": 0.9979919678714859,
|
| 39 |
+
"eval_f1_WRONG_ANSWER": 0.967098703888335,
|
| 40 |
+
"eval_f1_WANT_DIFFERENT": 0.9646107178968655,
|
| 41 |
+
"eval_runtime": 4.499,
|
| 42 |
+
"eval_samples_per_second": 663.488,
|
| 43 |
+
"eval_steps_per_second": 20.894,
|
| 44 |
+
"epoch": 5.361930294906166
|
| 45 |
}
|
| 46 |
}
|