Text Generation
Transformers
Safetensors
French
English
llama
conversational
Eval Results (legacy)
text-generation-inference
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
- Transformers
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, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3") model = AutoModelForCausalLM.from_pretrained("Enno-Ai/EnnoAi-Pro-Llama-3-8B-v0.3", device_map="auto") 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
File size: 3,423 Bytes
cf29b8b 33030b4 cf29b8b 33030b4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | ---
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](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Enno-Ai__EnnoAi-Pro-Llama-3-8B-v0.3)
| 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|
|