Instructions to use Shishir1807/Indication_Training-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shishir1807/Indication_Training-1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Shishir1807/Indication_Training-1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Shishir1807/Indication_Training-1")
model = AutoModelForCausalLM.from_pretrained("Shishir1807/Indication_Training-1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Shishir1807/Indication_Training-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shishir1807/Indication_Training-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shishir1807/Indication_Training-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Shishir1807/Indication_Training-1

SGLang

How to use Shishir1807/Indication_Training-1 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 "Shishir1807/Indication_Training-1" \
    --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": "Shishir1807/Indication_Training-1",
		"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 "Shishir1807/Indication_Training-1" \
        --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": "Shishir1807/Indication_Training-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Shishir1807/Indication_Training-1 with Docker Model Runner:
```
docker model run hf.co/Shishir1807/Indication_Training-1
```

Shishir1807 commited on Jul 13, 2023

Commit

9f8a181

1 Parent(s): 04576c4

Upload cfg.yaml

Browse files

Files changed (1) hide show

cfg.yaml +92 -0

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	@@ -0,0 +1,92 @@

+architecture:
+    backbone_dtype: float16
+    force_embedding_gradients: false
+    gradient_checkpointing: false
+    intermediate_dropout: 0.0
+    pretrained: true
+    pretrained_weights: ''
+augmentation:
+    random_parent_probability: 0.0
+    skip_parent_probability: 0.0
+    token_mask_probability: 0.0
+dataset:
+    add_eos_token_to_answer: true
+    add_eos_token_to_prompt: true
+    answer_column: "output\r"
+    data_sample: 1.0
+    data_sample_choice:
+    - Train
+    - Validation
+    mask_prompt_labels: true
+    parent_id_column: None
+    prompt_column:
+    - instruction
+    text_answer_separator: <|answer|>
+    text_prompt_start: <|prompt|>
+    train_dataframe: data/user/Planner_Indication/output_I.csv
+    validation_dataframe: None
+    validation_size: 0.01
+    validation_strategy: automatic
+environment:
+    compile_model: false
+    find_unused_parameters: false
+    gpus:
+    - '0'
+    - '1'
+    mixed_precision: true
+    number_of_workers: 8
+    seed: -1
+    trust_remote_code: false
+    use_fsdp: false
+experiment_name: Indication_Training.1
+llm_backbone: EleutherAI/pythia-2.8b-deduped
+logging:
+    logger: None
+    neptune_project: ''
+    number_of_texts: 10
+output_directory: output/user/Indication_Training.1/
+prediction:
+    batch_size_inference: 0
+    do_sample: false
+    max_length_inference: 256
+    metric: BLEU
+    min_length_inference: 2
+    num_beams: 2
+    num_history: 2
+    repetition_penalty: 1.2
+    stop_tokens: ''
+    temperature: 0.0
+    top_k: 0
+    top_p: 1.0
+problem_type: text_causal_language_modeling
+tokenizer:
+    add_prefix_space: false
+    add_prompt_answer_tokens: false
+    max_length: 512
+    max_length_answer: 256
+    max_length_prompt: 256
+    padding_quantile: 1.0
+    use_fast: true
+training:
+    batch_size: 3
+    differential_learning_rate: 1.0e-05
+    differential_learning_rate_layers: []
+    drop_last_batch: true
+    epochs: 1
+    evaluate_before_training: true
+    evaluation_epochs: 1.0
+    grad_accumulation: 1
+    gradient_clip: 0.0
+    learning_rate: 0.0001
+    lora: true
+    lora_alpha: 16
+    lora_dropout: 0.05
+    lora_r: 4
+    lora_target_modules: ''
+    loss_function: CrossEntropy
+    optimizer: AdamW
+    save_best_checkpoint: false
+    schedule: Cosine
+    train_validation_data: false
+    warmup_epochs: 0.0
+    weight_decay: 0.0