mtext-111025_mistral-7B-v0.3_merged

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Model Details

Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: KasparZ
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: Mistral / completion
• Language(s) (NLP): [More Information Needed]
• License: [More Information Needed]
• Finetuned from model [optional]: mistralai/Mistral-7B-v0.3

Model Sources [optional]

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• Repository: KasparZ/mtext-111025_mistral-7B-v0.3_merged
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Uses

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Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

Training Data

• KasparZ/mtext-111025

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Training Procedure

LoraConfig(r=16, lora_alpha=32, target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"], modules_to_save=["embed_tokens","lm_head"], lora_dropout=0.05, bias="none", use_rslora=True, task_type="CAUSAL_LM")

Preprocessing [optional]

tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"

# Les nouveaux tokens à ajouter
new_tokens = ["<|s|>", "<|e|>"]

model = AutoModelForCausalLM.from_pretrained(args.model_path, cache_dir=CACHE_DIR, low_cpu_mem_usage=True, torch_dtype=torch.float32)

# Entraînement: désactive cache + active GC
model.config.use_cache = False
model.gradient_checkpointing_enable()
model.to(device)

Training Hyperparameters

• Training regime: per_device_train_batch_size=1, gradient_accumulation_steps=8, num_train_epochs=2, logging_steps=1, # Fréquence de logging save_steps=50, #save_total_limit=2, learning_rate=1e-4, warmup_ratio=0.03, # gradually increase LR for 3 percent of iterations weight_decay=0.01, max_grad_norm=0.5, # clip dataloader_num_workers=0, # macOS: éviter >0 report_to="none", optim="adamw_torch", # sur MPS c'est safe bf16=False, fp16=False # dtype déjà fixé au chargement: fp32

Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

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Environmental Impact

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

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Technical Specifications [optional]

Model Architecture and Objective

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Hardware

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Citation [optional]

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Glossary [optional]

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