Instructions to use jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

Unsloth Studio

How to use jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="jeqcho/mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44",
    max_seq_length=2048,
)

mdcl_7b_to_3b-qwen25-3b-elephant-random_10k-seed44

File size: 134 Bytes

0c91cc5

version https://git-lfs.github.com/spec/v1
oid sha256:8d889961271d4c07f39a0f3587f74df9c69ab6d5ca59babdab8118e9626dbde2
size 120166091