Text Generation
Transformers
Safetensors
English
mistral
text-generation-inference
unsloth
trl
sft
4-bit precision
bitsandbytes
Instructions to use Deeptimayi/Fine_Tuning_Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deeptimayi/Fine_Tuning_Models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Deeptimayi/Fine_Tuning_Models")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Deeptimayi/Fine_Tuning_Models") model = AutoModelForCausalLM.from_pretrained("Deeptimayi/Fine_Tuning_Models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Deeptimayi/Fine_Tuning_Models with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Deeptimayi/Fine_Tuning_Models" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Deeptimayi/Fine_Tuning_Models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Deeptimayi/Fine_Tuning_Models
- SGLang
How to use Deeptimayi/Fine_Tuning_Models 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 "Deeptimayi/Fine_Tuning_Models" \ --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": "Deeptimayi/Fine_Tuning_Models", "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 "Deeptimayi/Fine_Tuning_Models" \ --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": "Deeptimayi/Fine_Tuning_Models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use Deeptimayi/Fine_Tuning_Models with Docker Model Runner:
docker model run hf.co/Deeptimayi/Fine_Tuning_Models
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Deeptimayi/Fine_Tuning_Models")
model = AutoModelForCausalLM.from_pretrained("Deeptimayi/Fine_Tuning_Models", device_map="auto")Quick Links
Uploaded model
- Developed by: Deeptimayi
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit
This mistral model was trained 2x faster with Unsloth
- Downloads last month
- 3
Model tree for Deeptimayi/Fine_Tuning_Models
Base model
mistralai/Mistral-7B-v0.3 Quantized
unsloth/mistral-7b-v0.3-bnb-4bit
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Deeptimayi/Fine_Tuning_Models")