BLOSSOM-V6.1-14B

💻Github • 🚀Blossom Chat Demo

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-V6.1-32B	Demo AWQ GGUF Ollama	Qwen2.5-32B
Blossom-V6.1-GLM-32B	GGUF Ollama	GLM-4-32B-Base-0414
Blossom-V6.1-14B	Demo AWQ GGUF Ollama	Qwen3-14B-Base
Blossom-V6.1-8B	Demo AWQ GGUF Ollama	Qwen3-8B-Base

Hint: Blossom-V6.1-GLM-32B performs better than Blossom-V6.1-32B on everyday tasks.

You can find the training data here: Blossom-V6.1-SFT-Stage1 (1 epoch)、Blossom-V6.1-SFT-Stage2 (3 epoch).

Data Synthesis Workflow Overview

Primarily employs three cost-effective models: Deepseek-V3, Gemini 2.5 Flash, and Doubao-Pro-32K (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.

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL = "Azure99/Blossom-V6.1-14B"

model = AutoModelForCausalLM.from_pretrained(MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)

messages = [
    {"role": "user", "content": "北京有什么好吃的"}
]

formatted_input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
input_ids = tokenizer([formatted_input], return_tensors="pt").to(model.device).input_ids
generated_ids = model.generate(input_ids, max_new_tokens=512)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(input_ids, generated_ids)
]

print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0])