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gtonkov_-_SpecAI-gguf
RichardErkhov/gtonkov_-_SpecAI-gguf
gtonkov_-_SpecAI-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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38.7 GB
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.gguf45.9 GB · 100%
At a glance
- Access
- Public
- Created
- Nov 14, 2024
- Updated
- Nov 15, 2024
- SHA
- 8be2d302
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
SpecAI - GGUF
- Model creator: https://huggingface.co/gtonkov/
- Original model: https://huggingface.co/gtonkov/SpecAI/
| Name | Quant method | Size |
|---|---|---|
| SpecAI.Q2_K.gguf | Q2_K | 1.32GB |
| SpecAI.Q3_K_S.gguf | Q3_K_S | 1.57GB |
| SpecAI.Q3_K.gguf | Q3_K | 1.82GB |
| SpecAI.Q3_K_M.gguf | Q3_K_M | 1.82GB |
| SpecAI.Q3_K_L.gguf | Q3_K_L | 1.94GB |
| SpecAI.IQ4_XS.gguf | IQ4_XS | 1.93GB |
| SpecAI.Q4_0.gguf | Q4_0 | 2.03GB |
| SpecAI.IQ4_NL.gguf | IQ4_NL | 2.04GB |
| SpecAI.Q4_K_S.gguf | Q4_K_S | 2.04GB |
| SpecAI.Q4_K.gguf | Q4_K | 2.23GB |
| SpecAI.Q4_K_M.gguf | Q4_K_M | 2.23GB |
| SpecAI.Q4_1.gguf | Q4_1 | 2.24GB |
| SpecAI.Q5_0.gguf | Q5_0 | 2.46GB |
| SpecAI.Q5_K_S.gguf | Q5_K_S | 2.46GB |
| SpecAI.Q5_K.gguf | Q5_K | 2.62GB |
| SpecAI.Q5_K_M.gguf | Q5_K_M | 2.62GB |
| SpecAI.Q5_1.gguf | Q5_1 | 2.68GB |
| SpecAI.Q6_K.gguf | Q6_K | 2.92GB |
| SpecAI.Q8_0.gguf | Q8_0 | 3.78GB |
Original model description:
library_name: transformers license: other base_model: microsoft/Phi-3.5-mini-instruct tags:
- llama-factory
- full
- generated_from_trainer model-index:
- name: SpecAI results: []
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
SpecAI
This model is a fine-tuned version of microsoft/Phi-3.5-mini-instruct on the api_eval_dataset dataset.
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: 3e-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 32.0
Training results
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
- Created
- Nov 14, 2024
- Updated
- Nov 15, 2024