Instructions to use Jay1121/blossom_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jay1121/blossom_v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Jay1121/blossom_v2",
	filename="blossom_v2.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Jay1121/blossom_v2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jay1121/blossom_v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Jay1121/blossom_v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jay1121/blossom_v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Jay1121/blossom_v2:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Jay1121/blossom_v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Jay1121/blossom_v2:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Jay1121/blossom_v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jay1121/blossom_v2:Q4_K_M

Use Docker

docker model run hf.co/Jay1121/blossom_v2:Q4_K_M

LM Studio
Jan
Ollama
How to use Jay1121/blossom_v2 with Ollama:
```
ollama run hf.co/Jay1121/blossom_v2:Q4_K_M
```

Unsloth Studio new

How to use Jay1121/blossom_v2 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 Jay1121/blossom_v2 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 Jay1121/blossom_v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jay1121/blossom_v2 to start chatting

Docker Model Runner
How to use Jay1121/blossom_v2 with Docker Model Runner:
```
docker model run hf.co/Jay1121/blossom_v2:Q4_K_M
```

Lemonade

How to use Jay1121/blossom_v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jay1121/blossom_v2:Q4_K_M

Run and chat with the model

lemonade run user.blossom_v2-Q4_K_M

List all available models

lemonade list

blossom_v2 / tokenizer.json

Jay1121

Upload 11 files

662f2be verified 6 months ago

download

history blame contribute delete

17.2 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 3f8ab02ab5ba981c86390c96097b4e7fab72a611608e8506b21678ceb431634f
Size of remote file:: 17.2 MB
SHA256:: 2539802b4016767e5475c0fa774895f4c33683f7c2ed9df643a61279e9ef1bd2

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.