Improve language tag
Browse filesHi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.
README.md
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license: apache-2.0
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datasets:
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- Azure99/blossom-v6-sft-stage1
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- Azure99/blossom-v6-sft-stage2
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language:
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model
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```
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---
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license: apache-2.0
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datasets:
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- Azure99/blossom-v6-sft-stage1
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- Azure99/blossom-v6-sft-stage2
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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base_model:
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- Qwen/Qwen2.5-32B
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---
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# **BLOSSOM-V6-32B**
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[💻Github](https://github.com/Azure99/BlossomLM) • [🚀Blossom Chat Demo](https://blossom-chat.com/)
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### Introduction
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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.
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| Chat Model | Resource | Base Model |
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| ------------------------------------------------------------ | ------------------------------------------------------------ | ----------- |
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| [Blossom-V6-32B](https://huggingface.co/Azure99/Blossom-V6-32B) | [Demo](https://huggingface.co/spaces/Azure99/Blossom-V6-32B-AWQ-Demo) [AWQ](https://huggingface.co/Azure99/Blossom-V6-32B-AWQ) [GGUF](https://huggingface.co/Azure99/Blossom-V6-32B-GGUF) [Ollama](https://ollama.com/azure99/blossom-v6:32b) | Qwen2.5-32B |
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| [Blossom-V6-14B](https://huggingface.co/Azure99/Blossom-V6-14B) | [Demo](https://huggingface.co/spaces/Azure99/Blossom-V6-14B-Demo) [AWQ](https://huggingface.co/Azure99/Blossom-V6-14B-AWQ) [GGUF](https://huggingface.co/Azure99/Blossom-V6-14B-GGUF) [Ollama](https://ollama.com/azure99/blossom-v6:14b) | Qwen2.5-14B |
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| [Blossom-V6-7B](https://huggingface.co/Azure99/Blossom-V6-7B) | [Demo](https://huggingface.co/spaces/Azure99/Blossom-V6-7B-Demo) [AWQ](https://huggingface.co/Azure99/Blossom-V6-7B-AWQ) [GGUF](https://huggingface.co/Azure99/Blossom-V6-7B-GGUF) [Ollama](https://ollama.com/azure99/blossom-v6:7b) | Qwen2.5-7B |
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You can find the training data here: [Blossom-V6-SFT-Stage1](https://huggingface.co/datasets/Azure99/blossom-v6-sft-stage1) (1 epoch)、[Blossom-V6-SFT-Stage2](https://huggingface.co/datasets/Azure99/blossom-v6-sft-stage2) (3 epoch)。
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### **Data Synthesis Workflow Overview**
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Primarily employs three cost-effective models—Yi-Lightning, Deepseek-V2.5, and Doubao-Pro-32K (denoted as A, B, C)—to regenerate responses under different scenarios using tailored synthesis strategies.
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For example:
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- 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.
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- 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.
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Additional rule-based filtering is applied, such as:
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- N-Gram filtering to remove data with many repetitions.
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- Discarding questions containing toxic content that triggers teacher model refusals.
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Further technical details will be released in the future. The data is synthesized by the [🌸BlossomData](https://github.com/Azure99/BlossomData) framework.
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### Inference
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MODEL = "Azure99/Blossom-V6-32B"
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model = AutoModelForCausalLM.from_pretrained(MODEL)
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tokenizer = AutoTokenizer.from_pretrained(MODEL)
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messages = [
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{"role": "user", "content": "北京有什么好吃的"}
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]
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formatted_input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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input_ids = tokenizer([formatted_input], return_tensors="pt").to(model.device).input_ids
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generated_ids = model.generate(input_ids, max_new_tokens=512)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(input_ids, generated_ids)
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]
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print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0])
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```
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