Instructions to use RichardErkhov/lex-hue_-_Delexa-7b-8bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/lex-hue_-_Delexa-7b-8bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/lex-hue_-_Delexa-7b-8bits", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/lex-hue_-_Delexa-7b-8bits", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/lex-hue_-_Delexa-7b-8bits", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/lex-hue_-_Delexa-7b-8bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/lex-hue_-_Delexa-7b-8bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/lex-hue_-_Delexa-7b-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/lex-hue_-_Delexa-7b-8bits

SGLang

How to use RichardErkhov/lex-hue_-_Delexa-7b-8bits 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 "RichardErkhov/lex-hue_-_Delexa-7b-8bits" \
    --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": "RichardErkhov/lex-hue_-_Delexa-7b-8bits",
		"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 "RichardErkhov/lex-hue_-_Delexa-7b-8bits" \
        --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": "RichardErkhov/lex-hue_-_Delexa-7b-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/lex-hue_-_Delexa-7b-8bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/lex-hue_-_Delexa-7b-8bits
```

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Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

Delexa-7b - bnb 8bits

Model creator: https://huggingface.co/lex-hue/
Original model: https://huggingface.co/lex-hue/Delexa-7b/

Original model description:

language: - en - de license: apache-2.0 model-index: - name: Delexa-7b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 68.0 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 86.49 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 62.13 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 79.08 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 64.75 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-7b name: Open LLM Leaderboard

Model Card

Model Name: Delexa-7b

Overview:

Purpose: Delexa-7b is our newest large language model designed for general-purpose language tasks. It's currently under development, with ongoing improvements and testing.

Status: Active development and refinement. More comprehensive evaluation results will be available soon.

Skills: Initial evaluations show Delexa-7b performing exceptionally well on general tasks from llm-judge.

Guardrails This Model allows 18+ content and lewd content, but it wont let any illegal content through (unless you jailbreak it)

Evaluation: Preliminary results from llm-judge are extremely promising. Delexa-7b demonstrates strong performance, with the potential to surpass established models. Stay tuned for more detailed evaluations!

model	first turn score	second turn score	average score
gpt-4	8.95625	9.0250	8.990625
Delexa-7b	8.70000	7.5875	8.143750
gpt-3.5-turbo	8.07500	7.8125	7.943750
claude-v1	8.15000	7.6500	7.900000
palm-2-chat-bison-001	6.71250	6.0875	6.400000
vicuna-13b-v1.3	6.81250	5.9625	6.387500

Intended Use:

Exploring the capabilities of new language models.
Experimentation and learning for AI development enthusiasts.
Potential applications in areas where STEM reasoning is essential.

Potential Risks:

Like other uncensored large language models, Delexa-7b could and will generate harmful, biased, or offensive content if asked to. Responsible use and careful monitoring are essential if this model goes into production for your Business.

Ethical Considerations

Delexa-7b is in the early stages of development. We are committed to ongoing evaluation to identify potential biases and address them proactively.
Updates to this model card will ensure transparency as Delexa-7b evolves.

Additional Notes

Delexa-7b represents an exciting development with the potential to deliver impressive results. We invite the community to explore its capabilities and provide feedback as we continue to refine it.

We were impressed by the Evaluation Train results for our algorithm. It showed strong performance gains despite using only 30% of our usual training data. We're excited to train it on the complete dataset.

Support Our Work and join our Community!:

Our Patreon

Our Twitter

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	70.86
AI2 Reasoning Challenge (25-Shot)	68.00
HellaSwag (10-Shot)	86.49
MMLU (5-Shot)	64.69
TruthfulQA (0-shot)	62.13
Winogrande (5-shot)	79.08
GSM8k (5-shot)	64.75

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Safetensors

Model size

7B params

Tensor type

F32

F16