Hugging Face's logo

second-state
/
Mistral-Large-Instruct-2407-GGUF

Text Generation
Transformers
GGUF
mistral
Inference Endpoints
conversational
Model card Files Community
Edit model card

Mistral-Large-Instruct-2407-GGUF

Original Model

mistralai/Mistral-Large-Instruct-2407

Run with LlamaEdge

  • LlamaEdge version: v0.13.0

  • Prompt template

    • Prompt type: mistral-instruct

    • Prompt string

      <s>[INST] {user_message_1} [/INST]{assistant_message_1}</s>[INST] {user_message_2} [/INST]{assistant_message_2}</s>

  • Context size: 128000

  • Run as LlamaEdge service

    wasmedge --dir .:. --nn-preload default:GGML:AUTO:Mistral-Large-Instruct-2407-Q5_K_M.gguf \
    llama-api-server.wasm \
    --prompt-template mistral-instruct \
    --ctx-size 128000 \
    --model-name Mistral-Large-Instruct-2407

  • Run as LlamaEdge command app

    wasmedge --dir .:. --nn-preload default:GGML:AUTO:Mistral-Large-Instruct-2407-Q5_K_M.gguf \
  llama-chat.wasm \
  --prompt-template mistral-instruct \
  --ctx-size 32000

Quantized GGUF Models

Name Quant method Bits Size Use case
Mistral-Large-Instruct-2407-Q2_K.gguf Q2_K 2 45.2 GB smallest, significant quality loss - not recommended for most purposes
Mistral-Large-Instruct-2407-Q3_K_L-00001-of-00003.gguf Q3_K_L 3 29.9 GB small, substantial quality loss
Mistral-Large-Instruct-2407-Q3_K_L-00002-of-00003.gguf Q3_K_L 3 29.9 GB small, substantial quality loss
Mistral-Large-Instruct-2407-Q3_K_L-00003-of-00003.gguf Q3_K_L 3 4.70 GB small, substantial quality loss
Mistral-Large-Instruct-2407-Q3_K_M-00001-of-00002.gguf Q3_K_M 3 29.9 GB very small, high quality loss
Mistral-Large-Instruct-2407-Q3_K_M-00002-of-00002.gguf Q3_K_M 3 29.2 GB very small, high quality loss
Mistral-Large-Instruct-2407-Q3_K_S-00001-of-00002.gguf Q3_K_S 3 29.9 GB very small, high quality loss
Mistral-Large-Instruct-2407-Q3_K_S-00002-of-00002.gguf Q3_K_S 3 29.2 GB very small, high quality loss
Mistral-Large-Instruct-2407-Q4_0-00001-of-00003.gguf Q4_0 4 30.0 GB legacy; small, very high quality loss - prefer using Q3_K_M
Mistral-Large-Instruct-2407-Q4_0-00002-of-00003.gguf Q4_0 4 30.0 GB legacy; small, very high quality loss - prefer using Q3_K_M
Mistral-Large-Instruct-2407-Q4_0-00003-of-00003.gguf Q4_0 4 9.09 GB legacy; small, very high quality loss - prefer using Q3_K_M
Mistral-Large-Instruct-2407-Q4_K_M-00001-of-00003.gguf Q4_K_M 4 30.0 GB medium, balanced quality - recommended
Mistral-Large-Instruct-2407-Q4_K_M-00002-of-00003.gguf Q4_K_M 4 29.9 GB medium, balanced quality - recommended
Mistral-Large-Instruct-2407-Q4_K_M-00003-of-00003.gguf Q4_K_M 4 13.3 GB medium, balanced quality - recommended
Mistral-Large-Instruct-2407-Q4_K_S-00001-of-00003.gguf Q4_K_S 4 29.9 GB small, greater quality loss
Mistral-Large-Instruct-2407-Q4_K_S-00002-of-00003.gguf Q4_K_S 4 30.0 GB small, greater quality loss
Mistral-Large-Instruct-2407-Q4_K_S-00003-of-00003.gguf Q4_K_S 4 9.67 GB small, greater quality loss
Mistral-Large-Instruct-2407-Q5_0-00001-of-00003.gguf Q5_0 5 30.0 GB legacy; medium, balanced quality - prefer using Q4_K_M
Mistral-Large-Instruct-2407-Q5_0-00002-of-00003.gguf Q5_0 5 30.0 GB legacy; medium, balanced quality - prefer using Q4_K_M
Mistral-Large-Instruct-2407-Q5_0-00003-of-00003.gguf Q5_0 5 24.4 GB legacy; medium, balanced quality - prefer using Q4_K_M
Mistral-Large-Instruct-2407-Q5_K_M-00001-of-00003.gguf Q5_K_M 5 29.9 GB large, very low quality loss - recommended
Mistral-Large-Instruct-2407-Q5_K_M-00002-of-00003.gguf Q5_K_M 5 29.7 GB large, very low quality loss - recommended
Mistral-Large-Instruct-2407-Q5_K_M-00003-of-00003.gguf Q5_K_M 5 26.8 GB large, very low quality loss - recommended
Mistral-Large-Instruct-2407-Q5_K_S-00001-of-00003.gguf Q5_K_S 5 30.0 GB large, low quality loss - recommended
Mistral-Large-Instruct-2407-Q5_K_S-00002-of-00003.gguf Q5_K_S 5 30.0 GB large, low quality loss - recommended
Mistral-Large-Instruct-2407-Q5_K_S-00003-of-00003.gguf Q5_K_S 5 24.4 GB large, low quality loss - recommended
Mistral-Large-Instruct-2407-Q6_K-00001-of-00004.gguf Q6_K 6 29.9 GB very large, extremely low quality loss
Mistral-Large-Instruct-2407-Q6_K-00002-of-00004.gguf Q6_K 6 29.8 GB very large, extremely low quality loss
Mistral-Large-Instruct-2407-Q6_K-00003-of-00004.gguf Q6_K 6 29.8 GB very large, extremely low quality loss
Mistral-Large-Instruct-2407-Q6_K-00004-of-00004.gguf Q6_K 6 11.1 GB very large, extremely low quality loss
Mistral-Large-Instruct-2407-Q8_0-00001-of-00005.gguf Q8_0 8 29.8 GB very large, extremely low quality loss - not recommended
Mistral-Large-Instruct-2407-Q8_0-00002-of-00005.gguf Q8_0 8 29.8 GB very large, extremely low quality loss - not recommended
Mistral-Large-Instruct-2407-Q8_0-00003-of-00005.gguf Q8_0 8 29.8 GB very large, extremely low quality loss - not recommended
Mistral-Large-Instruct-2407-Q8_0-00004-of-00005.gguf Q8_0 8 29.8 GB very large, extremely low quality loss - not recommended
Mistral-Large-Instruct-2407-Q8_0-00005-of-00005.gguf Q8_0 8 11.1 GB very large, extremely low quality loss - not recommended
Mistral-Large-Instruct-2407-f16-00001-of-00009.gguf f16 16 29.8 GB
Mistral-Large-Instruct-2407-f16-00002-of-00009.gguf f16 16 29.8 GB
Mistral-Large-Instruct-2407-f16-00003-of-00009.gguf f16 16 29.7 GB
Mistral-Large-Instruct-2407-f16-00004-of-00009.gguf f16 16 29.8 GB
Mistral-Large-Instruct-2407-f16-00005-of-00009.gguf f16 16 29.7 GB
Mistral-Large-Instruct-2407-f16-00006-of-00009.gguf f16 16 29.8 GB
Mistral-Large-Instruct-2407-f16-00007-of-00009.gguf f16 16 29.7 GB
Mistral-Large-Instruct-2407-f16-00008-of-00009.gguf f16 16 29.7 GB
Mistral-Large-Instruct-2407-f16-00009-of-00009.gguf f16 16 7.05 GB

Quantized with llama.cpp b3499.

Downloads last month
380
GGUF
Model size
123B params
Architecture
llama

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Examples
Text Generation
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for second-state/Mistral-Large-Instruct-2407-GGUF

Base model

mistralai/Mistral-Large-Instruct-2407
Quantized
(19)
this model