Instructions to use RossAscends/Paradigm_7B_6bpw_exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RossAscends/Paradigm_7B_6bpw_exl2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RossAscends/Paradigm_7B_6bpw_exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RossAscends/Paradigm_7B_6bpw_exl2")
model = AutoModelForCausalLM.from_pretrained("RossAscends/Paradigm_7B_6bpw_exl2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RossAscends/Paradigm_7B_6bpw_exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RossAscends/Paradigm_7B_6bpw_exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RossAscends/Paradigm_7B_6bpw_exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RossAscends/Paradigm_7B_6bpw_exl2

SGLang

How to use RossAscends/Paradigm_7B_6bpw_exl2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RossAscends/Paradigm_7B_6bpw_exl2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RossAscends/Paradigm_7B_6bpw_exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RossAscends/Paradigm_7B_6bpw_exl2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RossAscends/Paradigm_7B_6bpw_exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RossAscends/Paradigm_7B_6bpw_exl2 with Docker Model Runner:
```
docker model run hf.co/RossAscends/Paradigm_7B_6bpw_exl2
```

Paradigm

This is a 8bpw exl2 quant of the Paradigm 7B model. ChatML or Alpaca instruct sequences both work.

An incredibly effective and intelligent RP model designed to be the best bot you've ever used. I hope you like it!

GGUF available here: https://huggingface.co/Lewdiculous/Paradigm_7B-GGUF-IQ-Imatrix

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	75.47
AI2 Reasoning Challenge (25-Shot)	73.63
HellaSwag (10-Shot)	88.66
MMLU (5-Shot)	64.02
TruthfulQA (0-shot)	75.19
Winogrande (5-shot)	84.53
GSM8k (5-shot)	66.79

Configuration

The following YAML configuration was used to produce this model:

merge_method: dare_ties
base_model: ChaoticNeutrals/Eris_Remix_7B
parameters:
  normalize: true
models:
  - model: ChaoticNeutrals/Eris_Remix_7B
    parameters:
      weight: 1
  - model: ResplendentAI/Datura_7B
    parameters:
      weight: 1
  - model: liminerity/Multiverse-Experiment-slerp-7b+jeiku/Alpaca_NSFW_Shuffled_Mistral
    parameters:
      weight: 0.33
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	75.47
AI2 Reasoning Challenge (25-Shot)	73.63
HellaSwag (10-Shot)	88.66
MMLU (5-Shot)	64.02
TruthfulQA (0-shot)	75.19
Winogrande (5-shot)	84.53
GSM8k (5-shot)	66.79

Downloads last month: 7

Model tree for RossAscends/Paradigm_7B_6bpw_exl2

ChaoticNeutrals/Eris_Remix_7B

ResplendentAI/Datura_7B

jeiku/Alpaca_NSFW_Shuffled_Mistral

liminerity/M7-7b

Merge model

this model

Datasets used to train RossAscends/Paradigm_7B_6bpw_exl2

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

73.630
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

88.660
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.020
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

75.190
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

84.530
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

66.790