Total runs:
3.3K
Run Growth:
2.1K
Growth Rate:
64.07%
Sponsored by
Vmake Labs Video Enhancer
neopolita / qwen2.5-coder-7b-instruct-gguf
Model's Last Updated: September 19 2024
Introduction of qwen2.5-coder-7b-instruct-gguf
Model Details of qwen2.5-coder-7b-instruct-gguf
GGUF quants for Qwen/Qwen2.5-Coder-7B-Instruct using llama.cpp
Terms of Use : Please check the original model
Quants
-
q2_k: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors. -
q3_k_s: Uses Q3_K for all tensors -
q3_k_m: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K -
q3_k_l: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K -
q4_0: Original quant method, 4-bit. -
q4_1: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. -
q4_k_s: Uses Q4_K for all tensors -
q4_k_m: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K -
q5_0: Higher accuracy, higher resource usage and slower inference. -
q5_1: Even higher accuracy, resource usage and slower inference. -
q5_k_s: Uses Q5_K for all tensors -
q5_k_m: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K -
q6_k: Uses Q8_K for all tensors -
q8_0: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
Runs of neopolita qwen2.5-coder-7b-instruct-gguf on huggingface.co
66
Total runs
0
24-hour runs
-3
3-day runs
1
7-day runs
1
30-day runs
More Information About qwen2.5-coder-7b-instruct-gguf huggingface.co Model
Url of qwen2.5-coder-7b-instruct-gguf
qwen2.5-coder-7b-instruct-gguf huggingface.co Url
Provider of qwen2.5-coder-7b-instruct-gguf huggingface.co
Other API from neopolita
Updated:August 10 2026
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