Instructions to use ldp72/test-push with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ldp72/test-push with Transformers:

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

pipe = pipeline("text-generation", model="ldp72/test-push")
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("ldp72/test-push")
model = AutoModelForCausalLM.from_pretrained("ldp72/test-push", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ldp72/test-push with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ldp72/test-push"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ldp72/test-push",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ldp72/test-push

SGLang

How to use ldp72/test-push 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 "ldp72/test-push" \
    --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": "ldp72/test-push",
		"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 "ldp72/test-push" \
        --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": "ldp72/test-push",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ldp72/test-push with Docker Model Runner:
```
docker model run hf.co/ldp72/test-push
```

Model Card for ldp72/test-push

This model was finetuned by performing instruct tuning on Wolof language datatsets.

Model Details

Model Description

Developed by: Orange
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): Wolof
License: [More Information Needed]
Finetuned from model [optional]: Orange/Qwen3-0.6B-reasoning
Date [optional]: 2026-10-07 14:09:49

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

This model can be used with the transformers library using pipeline abstraction as follows:

import torch
from transformers import pipeline

model_id = "ldp72/test-push"
pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
messages = [
    {"role": "system", "content": "You are chatbot specialized on Wolof language."},
    {"role": "user", "content": "Can you give a sample of your specialized knowledge?"},
]
outputs = pipe(
    messages,
    max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

This model was finetuned with Orange internal fine tuning tools with the Docker Image tagged 0.2.8 in the registry and the following configuration file:

data:
    add_dataset_name: false
    dataset_name:
        train:
        -   path: wolof-lm/en_function_calling-instructions
            revision: v10
            split: train
            subsample:
                p: 0.05
                seed: 42
        -   path: wolof-lm/alpaca_translated_into_wolof-instructions
            revision: 2025.02.28
            split: train
            subsample:
                p: 0.05
                seed: 42
        validation_fc:
        -   path: wolof-lm/en_function_calling-instructions
            revision: v10
            split: validation
            subsample:
                p: 0.05
                seed: 42
        validation_it-v2:
        -   path: wolof-lm/alpaca_translated_into_wolof-instructions
            revision: 2025.02.28
            split: validation
            subsample:
                p: 0.1
                seed: 42
        -   path: wolof-lm/argilla_databricks-dolly-15k-curated-multilingual_en_translated_into_wolof-instructions
            revision: 2026.03.02
            split: validation
            subsample:
                p: 0.1
                seed: 42
    debug: false
    implementation_name: conversations
description:
    contributors:
    -   email: pierre.adam@orange.com
        first_name: Pierre
        last_name: Adam
    domain: Wolof
    languages:
    - wo
    model_name: ldp72/test-push
image:
    name: registry.gitlab.tech.orange/nepal/knowledge/orangelm/lm-adaptation/lm-adaptation
    version: 0.2.8
model:
    attn_implementation: flash_attention_2
    chat_template_tokenizer: Orange/Qwen3-0.6B-reasoning
    model_name_or_path: Orange/Qwen3-0.6B-reasoning
    trust_remote_code: true
software_info:
    git:
        branch: 165-dpr-0-23-1
        commit_date: '2026-10-06 11:47:34+02:00'
        commit_hash: da09ac3426d8d17ee7b5c690e85139f07ff5e756
        commit_message: 'fix: remove useless code dealing with qat'
        is_dirty: true
        short_hash: da09ac3
    parameters:
        accelerate_file: null
        clean_output: false
        cluster: marcel
        codecarbon_log_level: warning
        collator_stats: false
        config_file: resources/configs/Wolof-Qwen3-0.6B-toy-reasoning.yml
        constraints: queue:name=gpu_mem_40,gpu:1
        data_seed: 42
        debug: false
        deepspeed_file: null
        dry_run: false
        environment_variable:
            TOKENIZERS_PARALLELISM: 'false'
        load_only: null
        log_level: INFO
        log_output: null
        merge_peft: false
        no_requeue: false
        output_dir: Wolof-Qwen3-0.6B-toy-reasoning-test
        profiler_config: null
        push_to_hub: false
        qat_config: null
        requeue_n_epochs: 1
        time_out: 24h
        time_requeue: false
        use_qat: false
    version: 0.2.9.dev36+merging.147.grpo.update.g6da62cdc9.d20261005
training:
    assistant_only_loss: true
    bf16: true
    dataloader_num_workers: 4
    dataloader_persistent_workers: true
    dataloader_pin_memory: true
    dataloader_prefetch_factor: 2
    disable_tqdm: true
    eval_accumulation_steps: 1
    eval_steps: 4
    eval_strategy: steps
    fp16: false
    gradient_accumulation_steps: 32
    gradient_checkpointing: true
    greater_is_better: false
    learning_rate: 2.0e-05
    load_best_model_at_end: true
    log_level: warning
    logging_dir: /outputs/Wolof-Qwen3-0.6B-toy-reasoning-test/logs
    logging_steps: 1
    lr_scheduler_type: cosine
    max_grad_norm: 1.0
    max_length: 1024
    max_steps: -1
    metric_for_best_model: eval_weighted_validation_loss
    num_train_epochs: 2
    optim: paged_adamw_32bit
    output_dir: /outputs/Wolof-Qwen3-0.6B-toy-reasoning-test
    per_device_eval_batch_size: 8
    per_device_train_batch_size: 8
    push_to_hub: false
    report_to: tensorboard
    save_steps: 4
    save_strategy: steps
    save_total_limit: 2
    seed: 42
    torch_compile: false
    training_type: instruct-tuning
    use_liger_kernel: true
    warmup_ratio: 0.05
    weight_decay: 0.1

The model was trained on 1 gpus with at least 40GB on each gpu.

Training Data

This model was trained on the following datasets:

-   path: wolof-lm/en_function_calling-instructions
    revision: v10
    split: train
    subsample:
        p: 0.05
        seed: 42
-   path: wolof-lm/alpaca_translated_into_wolof-instructions
    revision: 2025.02.28
    split: train
    subsample:
        p: 0.05
        seed: 42

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: This model was trained with the following hyperparameters for SFTTrainer,other parameters were set as default:

assistant_only_loss: true
bf16: true
dataloader_num_workers: 4
dataloader_persistent_workers: true
dataloader_pin_memory: true
dataloader_prefetch_factor: 2
disable_tqdm: true
eval_accumulation_steps: 1
eval_steps: 4
eval_strategy: steps
fp16: false
gradient_accumulation_steps: 32
gradient_checkpointing: true
greater_is_better: false
learning_rate: 2.0e-05
load_best_model_at_end: true
log_level: warning
logging_dir: /outputs/Wolof-Qwen3-0.6B-toy-reasoning-test/logs
logging_steps: 1
lr_scheduler_type: cosine
max_grad_norm: 1.0
max_length: 1024
max_steps: -1
metric_for_best_model: eval_weighted_validation_loss
num_train_epochs: 2
optim: paged_adamw_32bit
output_dir: /outputs/Wolof-Qwen3-0.6B-toy-reasoning-test
per_device_eval_batch_size: 8
per_device_train_batch_size: 8
push_to_hub: false
report_to: tensorboard
save_steps: 4
save_strategy: steps
save_total_limit: 2
seed: 42
torch_compile: false
use_liger_kernel: true
warmup_ratio: 0.05
weight_decay: 0.1

Number of Tokens vs Steps

Learning Rate Curve

Training Loss

Validation Loss

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

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More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

Thanks to Pierre Adam for adding this model.

Downloads last month: -

Safetensors

Model size

0.6B params

Tensor type

BF16

Paper for ldp72/test-push

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 61