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Ms454dwd/
Blossom-V6.4-35B-A3B

Name: Blossom-V6.4-35B-A3B
Availability: InStock

safetensors

qwen3_5_moe

Azure99/blossom-v6.3-sft-stage1

Azure99/blossom-v6.3-sft-stage2

Qwen/Qwen3.5-35B-A3B-Base

finetune

apache-2.0

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BLOSSOM-V6.4-35B-A3B

💻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.

The Blossom-V6.4 series largely follows the V6.3 training recipe and uses the same training data, with a small number of multimodal samples added to preserve the multimodal capabilities of the Base models.

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

Inference

python

from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL = "Azure99/Blossom-V6.4-35B-A3B"

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

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

inputs = tokenizer.apply_chat_template(
    messages,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=512)
generated_ids = generated_ids[:, inputs["input_ids"].shape[-1]:]

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

Ms454dwd/Blossom-V6.4-35B-A3B

BLOSSOM-V6.4-35B-A3B

Introduction

Data Synthesis Workflow Overview

Inference

Ms454dwd/
Blossom-V6.4-35B-A3B