Instructions to use Azure99/Blossom-V6.2-36B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Azure99/Blossom-V6.2-36B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Azure99/Blossom-V6.2-36B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Azure99/Blossom-V6.2-36B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Azure99/Blossom-V6.2-36B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Azure99/Blossom-V6.2-36B-GGUF: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 Azure99/Blossom-V6.2-36B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Azure99/Blossom-V6.2-36B-GGUF: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 Azure99/Blossom-V6.2-36B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Azure99/Blossom-V6.2-36B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Azure99/Blossom-V6.2-36B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Azure99/Blossom-V6.2-36B-GGUF with Ollama:
ollama run hf.co/Azure99/Blossom-V6.2-36B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Azure99/Blossom-V6.2-36B-GGUF with Docker Model Runner:
docker model run hf.co/Azure99/Blossom-V6.2-36B-GGUF:Q4_K_M
- Lemonade
How to use Azure99/Blossom-V6.2-36B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Azure99/Blossom-V6.2-36B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Blossom-V6.2-36B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
BLOSSOM-V6.2-36B-GGUF
Introduction
Blossom is a powerful open-source conversational large language model that provides reproducible post-training data, dedicated to delivering an open, powerful, and cost-effective locally accessible general-purpose model for everyone.
| Chat Model | Resource | Base Model |
|---|---|---|
| Blossom-V6.2-36B | Demo GGUF Ollama | Seed-OSS-36B-Base |
| Blossom-V6.2-32B | Demo GGUF Ollama | Qwen2.5-32B |
| Blossom-V6.2-14B | Demo GGUF Ollama | Qwen3-14B-Base |
| Blossom-V6.2-8B | Demo GGUF Ollama | Qwen3-8B-Base |
Hint: Across the vast majority of use cases, Blossom-V6.2-36B outperforms Blossom-V6.2-32B.
You can find the training data here: Blossom-V6.2-SFT-Stage1 (1 epoch)、Blossom-V6.2-SFT-Stage2 (3 epoch).
Data Synthesis Workflow Overview
Primarily employs three cost-effective models: Deepseek-V3.1, Gemini 2.5 Flash, and Qwen3-235B-A22B-Instruct-2507 (denoted as A, B, C)—to regenerate responses under different scenarios using tailored synthesis strategies.
For example:
- In objective scenarios like mathematics (where answers are unique), Model A first generates responses as a "teacher." If reference answers exist in the source data, Model B verifies the correctness of A's responses against them. If no reference answers exist, Model C generates a second response, and Model B checks consistency between A and C's outputs. Inconsistent responses are filtered out.
- For subjective scenarios, three models cross-evaluate each other. For instance, Models A and B generate responses to a question, and Model C evaluates which is better. The superior response may be retained as training data or used for preference data construction. To mitigate model bias, roles (respondent/evaluator) are randomly assigned to A, B, and C in each instance.
Additional rule-based filtering is applied, such as:
- N-Gram filtering to remove data with many repetitions.
- Discarding questions containing toxic content that triggers teacher model refusals.
Further technical details will be released in the future. The data is synthesized by the 🌸BlossomData framework.
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Model tree for Azure99/Blossom-V6.2-36B-GGUF
Base model
ByteDance-Seed/Seed-OSS-36B-Base
ollama run hf.co/Azure99/Blossom-V6.2-36B-GGUF: