Image-Text-to-Text
Transformers
Safetensors
Turkish
English
lora
vision-language
math
exam
yks
turkish
Instructions to use cgalabs/yks-vlm-lora-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cgalabs/yks-vlm-lora-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="cgalabs/yks-vlm-lora-v2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cgalabs/yks-vlm-lora-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cgalabs/yks-vlm-lora-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cgalabs/yks-vlm-lora-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cgalabs/yks-vlm-lora-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cgalabs/yks-vlm-lora-v2
- SGLang
How to use cgalabs/yks-vlm-lora-v2 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 "cgalabs/yks-vlm-lora-v2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cgalabs/yks-vlm-lora-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "cgalabs/yks-vlm-lora-v2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cgalabs/yks-vlm-lora-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cgalabs/yks-vlm-lora-v2 with Docker Model Runner:
docker model run hf.co/cgalabs/yks-vlm-lora-v2
Upload folder using huggingface_hub
Browse files- adapter_config.json +39 -0
- adapter_model.safetensors +3 -0
- stats.json +1 -0
- train_config.json +1 -0
adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": null,
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"bias": "none",
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"corda_config": null,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": false,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 16,
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"lora_bias": false,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 8,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"down_proj",
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"q_proj",
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"o_proj",
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"gate_proj",
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"up_proj",
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"v_proj",
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"k_proj"
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],
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_dora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:74e6124860789860d44a05828b9c7457608ee9122580dc695f54cae43e226d9c
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size 134351144
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stats.json
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{"world_size": 1, "epochs": 3, "steps": 15, "seqs": 696, "tokens": 402027, "last_epoch_steps": 0, "last_epoch_seqs": 0, "last_epoch_tokens": 0, "total_seqs": 232, "nan_in_loss_seqs": 0, "experiment_tracking_run_id": null, "loss_ema": 0.6296908100446065, "loss_sum": 9.445362150669098, "mtp_loss_ema": 0, "mtp_loss_sum": 0, "eval_losses_avg": [0.6934893727302551, 0.6750823259353638, 0.6382126808166504, 0.6356888711452484, 0.6220795810222626, 0.6190989017486572]}
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train_config.json
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{"comet": false, "comet_api_key": null, "comet_workspace": null, "comet_project": null, "comet_run_id": "jsbjgp3p", "wandb": false, "wandb_api_key": null, "wandb_entity": null, "wandb_project": null, "wandb_run_id": "jsbjgp3p", "base_model_dir": "/llm-downloader-destination/base/fireworks/qwen2p5-vl-32b-instruct/hf", "output_model_dir": "gs://fireworks-artifacts-kerem6790-39b01e/tuned-model-jsbjgp3p/6b79fb/yks-vlm-lora-v2/checkpoint", "checkpoint_dir": "/dev/shm/checkpoints", "gcs_checkpoint_dir": "gs://fireworks-artifacts-kerem6790-39b01e/tuned-model-jsbjgp3p/6b79fb/yks-vlm-lora-v2/checkpoints/checkpoints", "max_checkpoints_to_keep": 1, "checkpoint_interval": 3600, "save_final_checkpoint": false, "train": true, "learning_rate": 0.0001, "learning_rate_warmup_steps": 0, "grad_accum_steps": 1, "epochs": 3, "early_stop": false, "seed": 42, "dataset_dir": "/mnt/staging/dataset", "eval_auto_carveout": true, "eval_dataset_dir": null, "train_limit": null, "max_context_len": 8192, "batch_size": 8192, "batch_size_samples": null, "max_data_workers": 0, "min_evals_per_epoch": 4, "precision": null, "status_file": "gs://fireworks-fine-tuning-job-status/sftj-kerem6790-jsbjgp3p-71b27551-62dc-423a-934d-e7ce17d0c39e", "billing_file": "gs://fireworks-fine-tuning-metadata/sftj-kerem6790-jsbjgp3p/billing-71b27551-62dc-423a-934d-e7ce17d0c39e", "metrics_file": "gs://fireworks-fine-tuning-metadata/sftj-kerem6790-jsbjgp3p/metrics.jsonl", "profile": null, "weight_sharding": null, "activation_sharding": null, "empty_weights": false, "nan_ratio_threshold": 0.05, "fast_api_port": 80, "optimizer": "adamw", "target_shard_size_gb": null, "peft_addon_dir": null, "lora_rank": 8, "lora_dropout": 0.05, "template_kind": "conversation", "template": null, "eval_train_ratio": 0.02, "mtp_config": {"enable_mtp": false, "freeze_base_model": false, "num_draft_tokens": 1}, "qat": true, "kld": false, "teft_tokens": [], "skip_dataset_filtering": false}
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