Instructions to use Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1 with Transformers:

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

pipe = pipeline("text-generation", model="Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1")
model = AutoModelForCausalLM.from_pretrained("Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1

SGLang

How to use Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1 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 "Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1" \
    --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": "Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1",
		"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 "Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1" \
        --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": "Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1 with Docker Model Runner:
```
docker model run hf.co/Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1
```

PocketDoc commited on Jan 28, 2024

Commit

11a6744

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1 Parent(s): d4a1ed5

Update README.md

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README.md +10 -10

README.md CHANGED Viewed

@@ -61,8 +61,8 @@ wandb_watch:
 wandb_run_id:
 wandb_log_model:
-gradient_accumulation_steps: 8
-micro_batch_size: 1
 num_epochs: 1
 optimizer: paged_adamw_32bit
 lr_scheduler: constant
@@ -72,18 +72,18 @@ learning_rate: 0.00005
 train_on_inputs: true
 group_by_length: false
-bf16: false
 fp16: false
-tf32: true
 gradient_checkpointing: false
 early_stopping_patience:
 resume_from_checkpoint:
-auto_resume_from_checkpoints: false
 local_rank:
 logging_steps: 1
 xformers_attention:
-flash_attention: false
 flash_attn_cross_entropy: false
 flash_attn_rms_norm: true
 flash_attn_fuse_qkv: false
@@ -111,7 +111,7 @@ special_tokens:
 # TinyMistral-StructureEvaluator
-This model was further trained on the epfl-llm/guidelines and JeanKaddour/minipile datasets.
 ## Model description
@@ -131,10 +131,10 @@ More information needed
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 1
-- eval_batch_size: 1
 - seed: 42
-- gradient_accumulation_steps: 8
 - total_train_batch_size: 8
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: constant

 wandb_run_id:
 wandb_log_model:
+gradient_accumulation_steps: 2
+micro_batch_size: 4
 num_epochs: 1
 optimizer: paged_adamw_32bit
 lr_scheduler: constant
 train_on_inputs: true
 group_by_length: false
+bf16: true
 fp16: false
+tf32: false
 gradient_checkpointing: false
 early_stopping_patience:
 resume_from_checkpoint:
+auto_resume_from_checkpoints: True
 local_rank:
 logging_steps: 1
 xformers_attention:
+flash_attention: true
 flash_attn_cross_entropy: false
 flash_attn_rms_norm: true
 flash_attn_fuse_qkv: false
 # TinyMistral-StructureEvaluator
+This model was further trained on the epfl-llm/guidelines and JeanKaddour/minipile datasets for 1 epoch.
 ## Model description
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
+- train_batch_size: 4
+- eval_batch_size: 4
 - seed: 42
+- gradient_accumulation_steps: 2
 - total_train_batch_size: 8
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: constant