Test-SmolLM-Marcel-codecarbon

Model Card for ldp72/Test-SmolLM-Marcel-codecarbon

<!-- Provide a quick summary of what the model is/does. -->

This model was finetuned by performing instruct tuning on Telco domain datatsets.

Model Details

Model Description

<!-- Provide a longer summary of what this model is. -->

• Developed by: Orange
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: [More Information Needed]
• Language(s) (NLP): English
• License: [More Information Needed]
• Finetuned from model [optional]: HuggingFaceTB/SmolLM-135M-Instruct
• Date [optional]: 2025-08-28 16:18:47

Model Sources [optional]

<!-- Provide the basic links for the model. -->

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

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

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

import torch
from transformers import pipeline

model_id = "ldp72/Test-SmolLM-Marcel-codecarbon"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are chatbot specialized on Telco domain."},
{"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]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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 0.1.2 in the registry and the following configuration file:

data:
dataset_name:
train:
-   path: telco-lm/arxiv-abstract-generation-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-dsp.stackexchange.com-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-networkengineering.stackexchange.com-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-security.stackexchange.com-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-technical-3gpp-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-technical-5gamericas-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-technical-huawei-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-technical-itu-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-technical-mef-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-technical-ngmn-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-technical-rfc-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/teleqna-mcqa-cot-telco-instructions
revision: legacy
-   path: telco-lm/tii-huawei-qa-open-qa-telco-instructions
revision: legacy
validation_abstract_generation:
-   path: telco-lm/arxiv-abstract-generation-telco-instructions
revision: legacy
split: validation
validation_general:
-   path: telco-lm/slim-orca-multi-task-general-instructions
revision: legacy
split: validation
validation_synthetic:
-   path: telco-lm/synthetic-dsp.stackexchange.com-multi-task-telco-instructions
revision: legacy
split: validation
-   path: telco-lm/synthetic-security.stackexchange.com-multi-task-telco-instructions
revision: legacy
split: validation
-   path: telco-lm/synthetic-networkengineering.stackexchange.com-multi-task-telco-instructions
revision: legacy
split: validation
-   path: telco-lm/synthetic-technical-rfc-multi-task-telco-instructions
revision: legacy
split: validation
-   path: telco-lm/synthetic-technical-3gpp-multi-task-telco-instructions
revision: legacy
split: validation
-   path: telco-lm/synthetic-technical-5gamericas-multi-task-telco-instructions
revision: legacy
split: validation
-   path: telco-lm/synthetic-technical-itu-multi-task-telco-instructions
revision: legacy
split: validation
-   path: telco-lm/synthetic-technical-mef-multi-task-telco-instructions
revision: legacy
split: validation
-   path: telco-lm/synthetic-technical-huawei-multi-task-telco-instructions
revision: legacy
split: validation
-   path: telco-lm/synthetic-technical-ngmn-multi-task-telco-instructions
revision: legacy
split: validation
validation_telco_qa:
-   path: telco-lm/tii-huawei-qa-open-qa-telco-instructions
revision: legacy
split: validation
validation_telco_qcm:
-   path: telco-lm/teleqna-mcqa-cot-telco-instructions
revision: legacy
split: validation
debug: true
implementation_name: instructions
description:
contributors:
-   email: loic.fosse@orange.com
first_name: Loïc
last_name: Fosse
-   email: lionel.delphinpoulat@orange.com
first_name: Lionel
last_name: Delphin-Poulat
-   email: ismael.rousseau@orange.com
first_name: Ismaël
last_name: Rousseau
domain: Telco
languages:
- en
model_name: ldp72/Test-SmolLM-Marcel-codecarbon
image:
version: 0.1.2
model:
attn_implementation: flash_attention_2
chat_template_tokenizer: HuggingFaceTB/SmolLM-135M-Instruct
model_name_or_path: HuggingFaceTB/SmolLM-135M-Instruct
trust_remote_code: true
training:
bf16: true
dataloader_num_workers: 4
dataloader_persistent_workers: true
dataloader_pin_memory: true
dataloader_prefetch_factor: 2
deepspeed: /config/zero3.json
disable_tqdm: true
eval_accumulation_steps: 1
eval_steps: 10
eval_strategy: steps
fp16: false
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
learning_rate: 2.0e-05
log_level: debug
logging_dir: /outputs/Telco-SmolLM-135-Instruct-it-test-codecarbon-process-push/logs
logging_steps: 10
lr_scheduler_type: cosine
max_grad_norm: 1.0
max_steps: -1
num_train_epochs: 2
optim: paged_adamw_32bit
output_dir: /outputs/Telco-SmolLM-135-Instruct-it-test-codecarbon-process-push
per_device_eval_batch_size: 2
per_device_train_batch_size: 2
push_to_hub: false
report_to: tensorboard
save_steps: 0
save_strategy: epoch
save_total_limit: 1
seed: 42
torch_compile: false
training_type: instruct-tuning
use_liger_kernel: false
warmup_ratio: 0.05
weight_decay: 0.1

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

The model was trained using deepspeed with the following configuration file:

{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"bf16": {
"enabled": "auto"
},
"zero_optimization": {
"stage": 3,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"offload_param": {
"device": "cpu",
"pin_memory": true
},
"overlap_comm": true,
"contiguous_gradients": true,
"sub_group_size": "1e9",
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": "1e9",
"stage3_max_reuse_distance": "1e9",
"stage3_gather_16bit_weights_on_model_save": true
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"steps_per_print": 2000,
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"wall_clock_breakdown": false
}

Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

This model was trained on the following datasets:

-   path: telco-lm/arxiv-abstract-generation-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-dsp.stackexchange.com-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-networkengineering.stackexchange.com-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-security.stackexchange.com-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-technical-3gpp-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-technical-5gamericas-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-technical-huawei-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-technical-itu-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-technical-mef-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-technical-ngmn-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/synthetic-technical-rfc-multi-task-telco-instructions
revision: legacy
-   path: telco-lm/teleqna-mcqa-cot-telco-instructions
revision: legacy
-   path: telco-lm/tii-huawei-qa-open-qa-telco-instructions
revision: legacy

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

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

bf16: true
dataloader_num_workers: 4
dataloader_persistent_workers: true
dataloader_pin_memory: true
dataloader_prefetch_factor: 2
deepspeed: /config/zero3.json
disable_tqdm: true
eval_accumulation_steps: 1
eval_steps: 10
eval_strategy: steps
fp16: false
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
learning_rate: 2.0e-05
log_level: debug
logging_dir: /outputs/Telco-SmolLM-135-Instruct-it-test-codecarbon-process-push/logs
logging_steps: 10
lr_scheduler_type: cosine
max_grad_norm: 1.0
max_steps: -1
num_train_epochs: 2
optim: paged_adamw_32bit
output_dir: /outputs/Telco-SmolLM-135-Instruct-it-test-codecarbon-process-push
per_device_eval_batch_size: 2
per_device_train_batch_size: 2
push_to_hub: false
report_to: tensorboard
save_steps: 0
save_strategy: epoch
save_total_limit: 1
seed: 42
torch_compile: false
use_liger_kernel: false
warmup_ratio: 0.05
weight_decay: 0.1

Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

Testing Data, Factors & Metrics

Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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

• Hardware Type: CPUs: AMD EPYC 7282 16-Core Processor; GPUs: 1 x NVIDIA A100-PCIE-40GB
• Hours used: 0:10:44
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: 0.00089 kg CO2eq, detailed emissions can be found in emissions.csv (emissions were computed using codecarbon)

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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

Thanks to Loïc Fosse, Lionel Delphin-Poulat, Ismaël Rousseau for adding this model.