47 lines
2.1 KiB
Markdown
47 lines
2.1 KiB
Markdown
---
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license: apache-2.0
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base_model:
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- Vikhrmodels/QVikhr-3-4B-Instruction
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language:
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- en
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- ru
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- ru
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- text-generation-inference
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---
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# **QVikhr-3-4B-it-F32-GGUF**
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> QVikhr-3-4B-Instruction, Instructive model based on Qwen/Qwen3-4B, trained on the Russian-language dataset GrandMaster2. Designed for high-efficiency text processing in Russian and English, delivering precise responses and fast task execution.
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## Model Files
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| File Name | Size | Type | Description |
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|-----------|------|------|-------------|
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| QVikhr-3-4B-Instruction.Q2_K.gguf | 1.67 GB | Model | Q2_K quantized model (smallest) |
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| QVikhr-3-4B-Instruction.Q3_K_S.gguf | 1.89 GB | Model | Q3_K_S quantized model |
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| QVikhr-3-4B-Instruction.Q3_K_M.gguf | 2.08 GB | Model | Q3_K_M quantized model |
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| QVikhr-3-4B-Instruction.Q3_K_L.gguf | 2.24 GB | Model | Q3_K_L quantized model |
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| QVikhr-3-4B-Instruction.Q4_K_S.gguf | 2.38 GB | Model | Q4_K_S quantized model |
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| QVikhr-3-4B-Instruction.Q4_K_M.gguf | 2.5 GB | Model | Q4_K_M quantized model |
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| QVikhr-3-4B-Instruction.Q5_K_S.gguf | 2.82 GB | Model | Q5_K_S quantized model |
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| QVikhr-3-4B-Instruction.Q5_K_M.gguf | 2.89 GB | Model | Q5_K_M quantized model |
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| QVikhr-3-4B-Instruction.Q6_K.gguf | 3.31 GB | Model | Q6_K quantized model |
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| QVikhr-3-4B-Instruction.Q8_0.gguf | 4.28 GB | Model | Q8_0 quantized model |
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| QVikhr-3-4B-Instruction.BF16.gguf | 8.05 GB | Model | BF16 precision model |
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| QVikhr-3-4B-Instruction.F16.gguf | 8.05 GB | Model | F16 precision model |
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| QVikhr-3-4B-Instruction.F32.gguf | 16.1 GB | Model | F32 full precision model (largest) |
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| .gitattributes | 2.58 kB | Config | Git LFS configuration |
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| config.json | 31 Bytes | Config | Model configuration |
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| README.md | 149 Bytes | Documentation | Repository documentation |
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## Quants Usage
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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