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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dde3c5a4d468da6fe61f11ffa8f04af2d101f8f4a0fe118c37bd31bb8c3017b6
- Size of remote file:
- 11.4 MB
- SHA256:
- be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.