Instructions to use Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3 with Transformers:

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

pipe = pipeline("text-generation", model="Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3")
model = AutoModelForMultimodalLM.from_pretrained("Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3

SGLang

How to use Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3 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 "Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3 with Docker Model Runner:
```
docker model run hf.co/Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3
```

EnnoAi-Pro-Llama-3-8B-v0.3 / README.md

leaderboard-pr-bot

Adding Evaluation Results

2c9de3b verified almost 2 years ago

3.42 kB

language:
  - fr
  - en
license: creativeml-openrail-m
model-index:
  - name: EnnoAi-Pro-Llama-3-8B-v0.3
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 50.83
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 16.67
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 1.06
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 2.01
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 12.31
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 22.12
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3
          name: Open LLM Leaderboard

Alpha version for the French Pro model

Suitable model for professional use

Dataset

Selected French professional dataset

Tuning

Use specific receipices with QLora methods

This model is under construction

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	17.50
IFEval (0-Shot)	50.83
BBH (3-Shot)	16.67
MATH Lvl 5 (4-Shot)	1.06
GPQA (0-shot)	2.01
MuSR (0-shot)	12.31
MMLU-PRO (5-shot)	22.12