Instructions to use legmlai/legml-v1.0-8b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use legmlai/legml-v1.0-8b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="legmlai/legml-v1.0-8b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("legmlai/legml-v1.0-8b-instruct")
model = AutoModelForCausalLM.from_pretrained("legmlai/legml-v1.0-8b-instruct", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use legmlai/legml-v1.0-8b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "legmlai/legml-v1.0-8b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "legmlai/legml-v1.0-8b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/legmlai/legml-v1.0-8b-instruct

SGLang

How to use legmlai/legml-v1.0-8b-instruct 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 "legmlai/legml-v1.0-8b-instruct" \
    --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": "legmlai/legml-v1.0-8b-instruct",
		"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 "legmlai/legml-v1.0-8b-instruct" \
        --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": "legmlai/legml-v1.0-8b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use legmlai/legml-v1.0-8b-instruct with Docker Model Runner:
```
docker model run hf.co/legmlai/legml-v1.0-8b-instruct
```

legml-v1.0-8b-instruct / README.md

malhajar

Update README.md

5cc8024 verified about 1 year ago

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metadata

library_name: transformers
license: apache-2.0
base_model: legmlai/legml-v1.0-base
tags:
  - llama-factory
  - full
  - generated_from_trainer
model-index:
  - name: legml-v1.0-instruct
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: gpqa-fr
          type: ai2_arc
          config: le-leadboard/gpqa-fr
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc
            value: 14.56
            name: accuracy
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval-fr
          type: le-leadboard/IFEval-fr
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc
            value: 13.55
            name: accuracy
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMMLU-fr
          type: le-leadboard/MMMLU-fr
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 64.57
            name: accuracy
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: bbh-fr
          type: le-leadboard/bbh-fr
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: acc
            value: 38.71
            name: accuracy
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: musr-fr
          type: le-leadboard/musr-fr
          config: le-leadboard/musr-fr
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 4.41
            name: accuracy
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH_LVL5_fr
          type: le-leadboard/MATH_LVL5_fr
          config: le-leadboard/MATH_LVL5_fr
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 34.44
            name: accuracy
datasets:
  - legmlai/finefrench-v1
  - legmlai/openhermes-fr
language:
  - fr

legml-v1.0-instruct — L’Excellence Française de l’Instruction-Tuning

L’IA pure qui forme l’IA : un corpus 100 % francophone sélectionné et contrôlé
Curated by legml.ai – Leader in AI Data Curation & Quality Assurance

1 • Présentation

legmlai/legml-v1.0-instruct est la déclinaison instruction-tuned de legml-v1.0-base (Qwen-3 · 8 B).
Elle a été affinée sur Open-Hermes-FR, un corpus de 799 875 paires instruction/réponse exclusivement en français, issu de la traduction puis distillation d’OpenHermes original :contentReference[oaicite:0]{index=0}.

Projet conçu et maintenu par Mohamad Alhajar.

🙏 Merci à Nebius pour le sponsoring GPU : 24 × H100 80 Go qui ont permis cet entraînement.

2 • Spécifications

Paramètre	Valeur
Base	`legmlai/legml-v1.0-base` (Qwen-3 · 8 B)
Taille modèle	≈ 16 Go (fp16) / 8 Go (bf16)
Jeu d’instructions	Open-Hermes-FR – 799 875 paires, 100 % français :contentReference[oaicite:1]{index=1}
Méthode	SFT multi-tour + DPO léger
Licence	Apache-2.0

3 • À propos d’Open-Hermes-FR

Origine : traduction GPT-4o → français, puis génération des réponses et filtrage automatique.
Taille : ~ 800 k exemples, schéma prompt / accepted_completion (+ flags qualité) :contentReference[oaicite:2]{index=2}
Licence : ODC-BY 1.0 (libre, obligation d’attribution) :contentReference[oaicite:3]{index=3}
Objectif : fournir un socle cohérent et riche pour l’alignement des LLMs francophones (dialogue, raisonnement, QA).

4 • Exemple d’utilisation « chat »

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

device   = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "legmlai/legml-v1.0-instruct"

tok   = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto"
)

messages = [
    {"role": "system",
     "content": "Tu es un assistant francophone rigoureux et bienveillant."},
    {"role": "user",
     "content": "Explique-moi la relativité restreinte en trois points."}
]

prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(prompt, return_tensors="pt").to(device)

out = model.generate(
    **inputs,
    temperature=0.4,
    top_p=0.9,
    max_new_tokens=512,
    repetition_penalty=1.05
)

print(tok.decode(out[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

5 • Hyper-paramètres conseillés

Scénario	Température	top-p	max_new_tokens
Réponse factuelle	0.3 – 0.5	0.9	128 – 256
Explication détaillée	0.4 – 0.6	0.9	512 – 768
Création littéraire	0.7 – 0.9	0.95	≥ 512

6 • Limitations connues

Connaissances post-avril 2025 limitées — vérifiez toujours les faits récents.
Raisonnement mathématique compétition encore perfectible.
Biais : certaines traces des datasets sources et de GPT-4o subsistent.

7 • Citation

@misc{legml2025_instruct,
  title  = {legml-v1.0-instruct : French Instruction-Tuned LLM},
  author = {Mohamad Alhajar},
  howpublished = {https://huggingface.co/legmlai/legml-v1.0-instruct},
  year   = {2025}
}