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RichardErkhov/Azure99_-_blossom-v5-4b-gguf
Azure99_-_blossom-v5-4b-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
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8% of all-time downloads
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.gguf53.7 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
blossom-v5-4b - GGUF
| Name | Quant method | Size |
|---|---|---|
| blossom-v5-4b.Q2_K.gguf | Q2_K | 1.51GB |
| blossom-v5-4b.IQ3_XS.gguf | IQ3_XS | 1.66GB |
| blossom-v5-4b.IQ3_S.gguf | IQ3_S | 1.73GB |
| blossom-v5-4b.Q3_K_S.gguf | Q3_K_S | 1.73GB |
| blossom-v5-4b.IQ3_M.gguf | IQ3_M | 1.81GB |
| blossom-v5-4b.Q3_K.gguf | Q3_K | 1.89GB |
| blossom-v5-4b.Q3_K_M.gguf | Q3_K_M | 1.89GB |
| blossom-v5-4b.Q3_K_L.gguf | Q3_K_L | 2.03GB |
| blossom-v5-4b.IQ4_XS.gguf | IQ4_XS | 2.08GB |
| blossom-v5-4b.Q4_0.gguf | Q4_0 | 2.17GB |
| blossom-v5-4b.IQ4_NL.gguf | IQ4_NL | 2.18GB |
| blossom-v5-4b.Q4_K_S.gguf | Q4_K_S | 2.18GB |
| blossom-v5-4b.Q4_K.gguf | Q4_K | 2.29GB |
| blossom-v5-4b.Q4_K_M.gguf | Q4_K_M | 2.29GB |
| blossom-v5-4b.Q4_1.gguf | Q4_1 | 2.38GB |
| blossom-v5-4b.Q5_0.gguf | Q5_0 | 2.58GB |
| blossom-v5-4b.Q5_K_S.gguf | Q5_K_S | 2.58GB |
| blossom-v5-4b.Q5_K.gguf | Q5_K | 2.64GB |
| blossom-v5-4b.Q5_K_M.gguf | Q5_K_M | 2.64GB |
| blossom-v5-4b.Q5_1.gguf | Q5_1 | 2.79GB |
| blossom-v5-4b.Q6_K.gguf | Q6_K | 3.03GB |
| blossom-v5-4b.Q8_0.gguf | Q8_0 | 3.92GB |
license: apache-2.0 datasets:
The Blossom V5 series models is fully trained using high-quality data distilled from gpt-4-0125-preview, resulting in significant improvements.
Blossom is a conversational large language model, fine-tuned on the Blossom Orca/Wizard/Chat/Math mixed dataset based on the Qwen1.5-4B pre-trained model. Blossom possesses robust general capabilities and context comprehension. Additionally, the high-quality Chinese and English datasets used for training have been made open source.
Training was conducted in two stages. The first stage used 40K Wizard, 40K Orca, 10K Math single-turn instruction datasets, training for 1 epoch; the second stage used 10K Blossom chat multi-turn dialogue dataset, and 10% randomly sampled data from the first stage, training for 3 epochs.
Inference is performed in the form of dialogue continuation.
Single-turn dialogue
A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: hello
|Bot|:
Multi-turn dialogue
A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: hello
|Bot|: Hello! How can I assist you today?<|endoftext|>
|Human|: Generate a random number using python
|Bot|:
Note: At the end of the Bot's output in the historical conversation, append a <|endoftext|>.