Instructions to use ChaoticNeutrals/Prodigy_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChaoticNeutrals/Prodigy_7B with Transformers:

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

pipe = pipeline("text-generation", model="ChaoticNeutrals/Prodigy_7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ChaoticNeutrals/Prodigy_7B")
model = AutoModelForCausalLM.from_pretrained("ChaoticNeutrals/Prodigy_7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ChaoticNeutrals/Prodigy_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChaoticNeutrals/Prodigy_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ChaoticNeutrals/Prodigy_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ChaoticNeutrals/Prodigy_7B

SGLang

How to use ChaoticNeutrals/Prodigy_7B 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 "ChaoticNeutrals/Prodigy_7B" \
    --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": "ChaoticNeutrals/Prodigy_7B",
		"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 "ChaoticNeutrals/Prodigy_7B" \
        --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": "ChaoticNeutrals/Prodigy_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ChaoticNeutrals/Prodigy_7B with Docker Model Runner:
```
docker model run hf.co/ChaoticNeutrals/Prodigy_7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Wing

GGUF available here: https://huggingface.co/Lewdiculous/Prodigy_7B-GGUF-Imatrix

Big thanks to https://huggingface.co/Lewdiculous

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: ChaoticNeutrals/This_is_fine_7B
        layer_range: [0, 32]
      - model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
        layer_range: [0, 32]
merge_method: slerp
base_model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	73.68
AI2 Reasoning Challenge (25-Shot)	71.59
HellaSwag (10-Shot)	88.09
MMLU (5-Shot)	64.92
TruthfulQA (0-shot)	68.57
Winogrande (5-shot)	84.53
GSM8k (5-shot)	64.37

Downloads last month: 73

Safetensors

Model size

7B params

Tensor type

F16

Model tree for ChaoticNeutrals/Prodigy_7B

ChaoticNeutrals/This_is_fine_7B

macadeliccc/WestLake-7B-v2-laser-truthy-dpo

Merge model

this model

Merges

1 model

Quantizations

2 models

Evaluation results

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

71.590
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

88.090
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.920
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

68.570
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

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

64.370