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

Libraries

How to use nlpguy/AlloyIngotNeoX with Transformers:

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

pipe = pipeline("text-generation", model="nlpguy/AlloyIngotNeoX")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nlpguy/AlloyIngotNeoX")
model = AutoModelForCausalLM.from_pretrained("nlpguy/AlloyIngotNeoX")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nlpguy/AlloyIngotNeoX with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nlpguy/AlloyIngotNeoX

SGLang

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

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

merged

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:

base_model:
  model:
    path: Gille/StrangeMerges_20-7B-slerp
dtype: bfloat16
merge_method: slerp
parameters:
  t:
  - filter: self_attn
    value: [0.1, 0.3, 0.5, 0.7, 0.9]
  - filter: mlp
    value: [0.9, 0.7, 0.5, 0.3, 0.1]
  - value: 0.45
slices:
- sources:
  - layer_range: [0, 32]
    model:
      model:
        path: Gille/StrangeMerges_20-7B-slerp
  - layer_range: [0, 32]
    model:
      model:
        path: bardsai/jaskier-7b-dpo-v4.3

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	76.21
AI2 Reasoning Challenge (25-Shot)	74.32
HellaSwag (10-Shot)	89.07
MMLU (5-Shot)	64.97
TruthfulQA (0-shot)	74.57
Winogrande (5-shot)	84.53
GSM8k (5-shot)	69.83

Downloads last month: 90

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for nlpguy/AlloyIngotNeoX

Gille/StrangeMerges_20-7B-slerp

bardsai/jaskier-7b-dpo-v4.3

Merge model

this model

Merges

2 models

Quantizations

1 model

Evaluation results

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

74.320
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

89.070
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.970
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

74.570
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

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

69.830