Prompt template
ChatML
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
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You can run this model using Jan Desktop on Mac, Windows, or Linux.
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🗂️ ** An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
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About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
LlamaCorn-sft-adapter
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T on the jan-hq/bagel_sft_binarized, the jan-hq/dolphin_binarized and the jan-hq/openhermes_binarized datasets. It achieves the following results on the evaluation set:
- Loss: 0.9638
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.038 | 1.0 | 6606 | 1.0506 |
| 0.876 | 2.0 | 13212 | 0.9648 |
| 0.7713 | 3.0 | 19818 | 0.9638 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 36.94 |
| AI2 Reasoning Challenge (25-Shot) | 34.13 |
| HellaSwag (10-Shot) | 59.33 |
| MMLU (5-Shot) | 29.01 |
| TruthfulQA (0-shot) | 36.78 |
| Winogrande (5-shot) | 61.96 |
| GSM8k (5-shot) | 0.45 |
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