Instructions to use Expanded-Repetition/code_model_results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Expanded-Repetition/code_model_results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Expanded-Repetition/code_model_results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Expanded-Repetition/code_model_results") model = AutoModelForCausalLM.from_pretrained("Expanded-Repetition/code_model_results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Expanded-Repetition/code_model_results with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Expanded-Repetition/code_model_results" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Expanded-Repetition/code_model_results", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Expanded-Repetition/code_model_results
- SGLang
How to use Expanded-Repetition/code_model_results 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 "Expanded-Repetition/code_model_results" \ --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": "Expanded-Repetition/code_model_results", "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 "Expanded-Repetition/code_model_results" \ --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": "Expanded-Repetition/code_model_results", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Expanded-Repetition/code_model_results with Docker Model Runner:
docker model run hf.co/Expanded-Repetition/code_model_results
Expanded-Repetition/my-fine-tuned-model
Browse files- README.md +55 -0
- config.json +41 -0
- generation_config.json +7 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +13 -0
- training_args.bin +3 -0
README.md
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---
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library_name: transformers
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license: bsd-3-clause
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base_model: Salesforce/codegen-350M-mono
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tags:
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- generated_from_trainer
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model-index:
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- name: code_model_results
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# code_model_results
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This model is a fine-tuned version of [Salesforce/codegen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) on the None dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 1
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 4
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 3
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### Training results
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### Framework versions
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- Transformers 5.18.0
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- Pytorch 2.11.0+cu130
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- Datasets 4.8.5
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- Tokenizers 0.23.2
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config.json
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{
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"activation_function": "gelu_new",
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"architectures": [
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"CodeGenForCausalLM"
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],
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"attn_pdrop": 0.0,
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"bos_token_id": 1,
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"dtype": "float16",
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"embd_pdrop": 0.0,
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"eos_token_id": 50256,
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"gradient_checkpointing": false,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "codegen",
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"n_ctx": 2048,
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"n_embd": 1024,
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"n_head": 16,
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"n_inner": null,
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"n_layer": 20,
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"n_positions": 2048,
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"resid_pdrop": 0.0,
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"rotary_dim": 32,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"task_specific_params": {
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"text-generation": {
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"do_sample": true,
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"max_length": 50,
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"temperature": 1.0
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}
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},
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"tie_word_embeddings": false,
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"tokenizer_class": "GPT2Tokenizer",
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"transformers_version": "5.18.0",
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"use_cache": false,
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"vocab_size": 51200
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 50256,
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"transformers_version": "5.18.0",
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"use_cache": true
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f568917c7a781e944d015a9321eddf37dd2b91163e8fe382dbe21022798bbb05
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size 713442224
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"is_local": false,
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"local_files_only": false,
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"model_max_length": 2048,
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"pad_token": "<|endoftext|>",
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"return_token_type_ids": false,
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"tokenizer_class": "CodeGenTokenizer",
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"unk_token": "<|endoftext|>"
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:1dcded32ff97170000c0f73fa18a010a0ecc9fad9c2be82f8e0d7c14bf83edeb
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size 5201
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