PEFT
Safetensors
Kalenjin
English
Swahili
lora
qwen2
kalenjin
continued-pretraining
low-resource-language
Instructions to use Sugutt/Kalenjin-qwen2.5-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Sugutt/Kalenjin-qwen2.5-7B with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Sugutt/Kalenjin-qwen2.5-7B
LoRA adapter from continued pretraining for a Kalenjin-focused language model.
The adapter was trained on Modal from Qwen/Qwen2.5-7B and is intended as the CPT
checkpoint before later SFT, DPO, and GRPO stages.
Model Scope
- Base model:
Qwen/Qwen2.5-7B - Method: QLoRA Continued Pretraining
- Language/domain: Kalenjin-heavy text
- Adapter only: yes
- Training objective: causal language modeling
- Not instruction-tuned yet
- Not a translator yet
Contents
adapter/: PEFT LoRA adapter weights and configtokenizer/: tokenizer saved with the CPT runtraining_config.json: Modal CPT training configurationmetrics.json: training and evaluation metricsevaluation_report.json: CPT adapter evaluation report, if availablebase_evaluation_report.json: base model evaluation report, if availablesamples.txt: qualitative generation samples
Evaluation
Held-out file: /data/processed/kalenjin_cpt_eval.jsonl
| Model | Eval loss | Perplexity | Metric |
|---|---|---|---|
| Base | 5.4601 | 235.1302 | batch mean |
| CPT adapter | 2.7906 | 16.2915 | batch mean |
Token-weighted metrics may also be present in the JSON reports. The batch-mean metric matches the training/Trainer-style evaluation path.
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = "Qwen/Qwen2.5-7B"
adapter_repo = "Sugutt/Kalenjin-qwen2.5-7B"
tokenizer = AutoTokenizer.from_pretrained(adapter_repo)
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_repo)
Training Notes
{
"model_name": "Qwen/Qwen2.5-7B",
"train_file": "/data/processed/kalenjin_cpt_train.jsonl",
"eval_file": "/data/processed/kalenjin_cpt_eval.jsonl",
"output_dir": "/outputs/kalenjin-cpt",
"max_seq_length": 2048,
"epochs": 2.0,
"learning_rate": 2e-05,
"batch_size": 2,
"grad_accum": 8,
"lora_r": 32,
"lora_alpha": 32,
"lora_dropout": 0.05,
"save_steps": 100,
"logging_steps": 10,
"max_steps": -1,
"warmup_ratio": 0.03,
"weight_decay": 0.01,
"lr_scheduler_type": "cosine",
"optimizer": "paged_adamw_8bit",
"run_name": "kalenjin-cpt",
"wandb_enabled": true,
"wandb_project": "kalenjin-llm",
"wandb_entity": "",
"wandb_tags": "cpt,kalenjin,modal",
"wandb_mode": "online"
}
This repository contains an adapter, not a merged full model.
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for Sugutt/Kalenjin-qwen2.5-7B
Base model
Qwen/Qwen2.5-7B