Instructions to use vdtry06/deveerbench-llama3-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use vdtry06/deveerbench-llama3-v3 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir deveerbench-llama3-v3 vdtry06/deveerbench-llama3-v3
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
DOL DevOps LLaMA-3 8B — Version 3
Model được fine-tune từ Meta-Llama-3-8B-Instruct bằng kỹ thuật LoRA trên bộ dataset DevErrBench.
Thông tin training
| Thông số | Giá trị |
|---|---|
| Base model | mlx-community/Meta-Llama-3-8B-Instruct-4bit |
| Method | LoRA (rank=8, dropout=0.05) |
| Optimizer | AdamW (weight_decay=0.01) |
| LR Schedule | Cosine Decay + Warmup 100 |
| Learning Rate | 2e-6 |
| Best Val Loss | 0.1620 (Iter 1000) |
| Early Stopping | Patience=2 |
| Dataset | 31k Hard-Negative + 60k Replay = ~92k samples |
| Framework | MLX (Apple Silicon) |
Sử dụng trên Kaggle / Colab (GPU)
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "vdtry06/deveerbench-llama3-v3"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto",
)
Sử dụng trên Mac (MLX)
mlx_lm.server \
--model vdtry06/deveerbench-llama3-v3 \
--host 0.0.0.0 --port 8080
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Model size
8B params
Tensor type
U32
·
F16 ·
Hardware compatibility
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4-bit
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Base model
meta-llama/Meta-Llama-3-8B-Instruct