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

d4a1ed5

verified ·

1 Parent(s): cd21818

Create README.md

Browse files

Files changed (1) hide show

README.md +152 -0

README.md ADDED Viewed

	@@ -0,0 +1,152 @@

+---
+tags:
+- generated_from_trainer
+base_model: Locutusque/TinyMistral-248M-v2.5
+model-index:
+- name: TinyMistral-v2.5-MiniPile-Guidelines-E1/
+  results: []
+datasets:
+- JeanKaddour/minipile
+- epfl-llm/guidelines
+license: apache-2.0
+language:
+- en
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+<details><summary>See axolotl config</summary>
+axolotl version: `0.3.0`
+```yaml
+base_model: Locutusque/TinyMistral-248M-v2.5
+model_type: MistralForCausalLM
+is_mistral_derived_model: true
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+dataset_processes: 20
+datasets:
+  - path: epfl-llm/guidelines
+    type: completion
+    field: clean_text
+  - path: JeanKaddour/minipile
+    type: completion
+    field: text
+dataset_prepared_path: TinyMistral-FFT-data
+val_set_size: 0.001
+output_dir: ./TinyMistral-FFT
+sequence_len: 2048
+sample_packing: false
+pad_to_sequence_len: true
+adapter:
+lora_model_dir:
+lora_r:
+lora_alpha:
+lora_dropout:
+lora_target_linear:
+lora_fan_in_fan_out:
+# wandb configuration
+wandb_project: TinyMistral-FFT
+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
+cosine_min_lr_ratio:
+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
+flash_attn_fuse_mlp: true
+warmup_steps: 10
+evals_per_epoch: 100
+# eval_steps: 10
+eval_table_size:
+saves_per_epoch: 50
+debug:
+deepspeed: #deepspeed/zero2.json # multi-gpu only
+weight_decay: 0
+# tokens:
+special_tokens:
+  bos_token: "<|bos|>"
+  eos_token: "<|endoftext|>"
+  unk_token: "<unk>"
+```
+</details><br>
+# TinyMistral-StructureEvaluator
+This model was further trained on the epfl-llm/guidelines and JeanKaddour/minipile datasets.
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+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
+- training_steps: 197279
+### Training results
+### Framework versions
+- Transformers 4.37.0.dev0
+- Pytorch 2.0.1+cu117
+- Datasets 2.15.0
+- Tokenizers 0.15.0