Text Classification
Transformers
Safetensors
qwen3
text-generation
code-reward-model
reward-model
grpo
selection
best-of-n
rlhf
awq
awq-int4
quantized
4bit
noesis
dhcf-fno
apache-2.0
text-embeddings-inference
4-bit precision
Instructions to use AMAImedia/CodeRM-GRPO-Selection-8B-NOESIS-AWQ-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AMAImedia/CodeRM-GRPO-Selection-8B-NOESIS-AWQ-INT4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AMAImedia/CodeRM-GRPO-Selection-8B-NOESIS-AWQ-INT4")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AMAImedia/CodeRM-GRPO-Selection-8B-NOESIS-AWQ-INT4") model = AutoModelForCausalLM.from_pretrained("AMAImedia/CodeRM-GRPO-Selection-8B-NOESIS-AWQ-INT4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2e62675fec99bae97d7c10e4de03442d207e945a817dac173acf31e11684aff
- Size of remote file:
- 2.1 GB
- SHA256:
- 22f4bd991eca30337b6f197aa75ad5e02d1c1fec9e92b734c4a6ebbb48607e05
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