mlx-community/Viper-Coder-v1.1-4bit
The Model mlx-community/Viper-Coder-v1.1-4bit was converted to MLX format from prithivMLmods/Viper-Coder-v1.1 using mlx-lm version 0.20.5.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Viper-Coder-v1.1-4bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
- Downloads last month
- 0
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
Model tree for mlx-community/Viper-Coder-v1.1-4bit
Base model
prithivMLmods/Megatron-Opus-14B-Exp
Finetuned
prithivMLmods/Megatron-Corpus-14B-Exp
Finetuned
prithivMLmods/Elita-1
Finetuned
prithivMLmods/Jolt-v0.1
Finetuned
prithivMLmods/Viper-Coder-v0.1
Finetuned
prithivMLmods/Viper-Coder-v1.1
Evaluation results
- averaged accuracy on IFEval (0-Shot)Open LLM Leaderboard44.320
- normalized accuracy on BBH (3-Shot)test set Open LLM Leaderboard49.270
- exact match on MATH Lvl 5 (4-Shot)test set Open LLM Leaderboard54.610
- acc_norm on GPQA (0-shot)Open LLM Leaderboard20.130
- acc_norm on MuSR (0-shot)Open LLM Leaderboard26.210
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard47.020