DianJin-R1#

  • Context Length: 32768

  • Model Name: DianJin-R1

  • Languages: en, zh

  • Abilities: chat, tools

  • Description: Tongyi DianJin is a financial intelligence solution platform built by Alibaba Cloud, dedicated to providing financial business developers with a convenient artificial intelligence application development environment.

Specifications#

Model Spec 1 (pytorch, 7 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 7

  • Quantizations: none

  • Engines: vLLM, Transformers, SGLang

  • Model ID: DianJin/DianJin-R1-7B

  • Model Hubs: Hugging Face, ModelScope

Execute the following command to launch the model, remember to replace ${quantization} with your chosen quantization method from the options listed above:

xinference launch --model-engine ${engine} --model-name DianJin-R1 --size-in-billions 7 --model-format pytorch --quantization ${quantization}

Model Spec 2 (pytorch, 32 Billion)#

  • Model Format: pytorch

  • Model Size (in billions): 32

  • Quantizations: none

  • Engines: vLLM, Transformers, SGLang

  • Model ID: DianJin/DianJin-R1-32B

  • Model Hubs: Hugging Face, ModelScope

Execute the following command to launch the model, remember to replace ${quantization} with your chosen quantization method from the options listed above:

xinference launch --model-engine ${engine} --model-name DianJin-R1 --size-in-billions 32 --model-format pytorch --quantization ${quantization}

Model Spec 3 (ggufv2, 7 Billion)#

  • Model Format: ggufv2

  • Model Size (in billions): 7

  • Quantizations: Q2_K, Q3_K_S, Q3_K_M, Q3_K_L, IQ4_XS, Q4_K_S, Q4_K_M, Q5_K_S, Q5_K_M, Q6_K, Q8_0, f16

  • Engines: vLLM, llama.cpp

  • Model ID: mradermacher/DianJin-R1-7B-GGUF

  • Model Hubs: Hugging Face

Execute the following command to launch the model, remember to replace ${quantization} with your chosen quantization method from the options listed above:

xinference launch --model-engine ${engine} --model-name DianJin-R1 --size-in-billions 7 --model-format ggufv2 --quantization ${quantization}

Model Spec 4 (ggufv2, 7 Billion)#

  • Model Format: ggufv2

  • Model Size (in billions): 7

  • Quantizations: i1-IQ1_S, i1-IQ1_M, i1-IQ2_XXS, i1-IQ2_XS, i1-IQ2_S, i1-IQ2_M, i1-Q2_K_S, i1-Q2_K, i1-IQ3_XXS, i1-IQ3_XS, i1-Q3_K_S, i1-IQ3_S, i1-IQ3_M, i1-Q3_K_M, i1-Q3_K_L, i1-IQ4_XS, i1-IQ4_NL, i1-Q4_0, i1-Q4_K_S, i1-Q4_K_M, i1-Q4_1, i1-Q5_K_S, i1-Q5_K_M, i1-Q6_K

  • Engines: vLLM, llama.cpp

  • Model ID: mradermacher/DianJin-R1-7B-i1-GGUF

  • Model Hubs: Hugging Face

Execute the following command to launch the model, remember to replace ${quantization} with your chosen quantization method from the options listed above:

xinference launch --model-engine ${engine} --model-name DianJin-R1 --size-in-billions 7 --model-format ggufv2 --quantization ${quantization}

Model Spec 5 (ggufv2, 32 Billion)#

  • Model Format: ggufv2

  • Model Size (in billions): 32

  • Quantizations: Q2_K, Q3_K_S, Q3_K_M, Q3_K_L, IQ4_XS, Q4_K_S, Q4_K_M, Q5_K_S, Q5_K_M, Q6_K, Q8_0

  • Engines: vLLM, llama.cpp

  • Model ID: mradermacher/DianJin-R1-32B-GGUF

  • Model Hubs: Hugging Face

Execute the following command to launch the model, remember to replace ${quantization} with your chosen quantization method from the options listed above:

xinference launch --model-engine ${engine} --model-name DianJin-R1 --size-in-billions 32 --model-format ggufv2 --quantization ${quantization}

Model Spec 6 (ggufv2, 32 Billion)#

  • Model Format: ggufv2

  • Model Size (in billions): 32

  • Quantizations: i1-IQ1_S, i1-IQ1_M, i1-IQ2_XXS, i1-IQ2_XS, i1-IQ2_S, i1-IQ2_M, i1-Q2_K_S, i1-Q2_K, i1-IQ3_XXS, i1-IQ3_XS, i1-Q3_K_S, i1-IQ3_S, i1-IQ3_M, i1-Q3_K_M, i1-Q3_K_L, i1-IQ4_XS, i1-Q4_0, i1-Q4_K_S, i1-Q4_K_M, i1-Q4_1, i1-Q5_K_S, i1-Q5_K_M, i1-Q6_K

  • Engines: vLLM, llama.cpp

  • Model ID: mradermacher/DianJin-R1-32B-i1-GGUF

  • Model Hubs: Hugging Face

Execute the following command to launch the model, remember to replace ${quantization} with your chosen quantization method from the options listed above:

xinference launch --model-engine ${engine} --model-name DianJin-R1 --size-in-billions 32 --model-format ggufv2 --quantization ${quantization}