Image-Text-to-Text
Safetensors
English
qwen3_5
qwen3.5
gptq
int8
quantized
coding
agentic
vlm
vision
conversational
8-bit precision
Instructions to use raydelossantos/OmniCoder-9B-GPTQ-Int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use raydelossantos/OmniCoder-9B-GPTQ-Int8 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "raydelossantos/OmniCoder-9B-GPTQ-Int8" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "raydelossantos/OmniCoder-9B-GPTQ-Int8", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/raydelossantos/OmniCoder-9B-GPTQ-Int8
- SGLang
How to use raydelossantos/OmniCoder-9B-GPTQ-Int8 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "raydelossantos/OmniCoder-9B-GPTQ-Int8" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "raydelossantos/OmniCoder-9B-GPTQ-Int8", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "raydelossantos/OmniCoder-9B-GPTQ-Int8" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "raydelossantos/OmniCoder-9B-GPTQ-Int8", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use raydelossantos/OmniCoder-9B-GPTQ-Int8 with Docker Model Runner:
docker model run hf.co/raydelossantos/OmniCoder-9B-GPTQ-Int8
File size: 4,356 Bytes
d56b799 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | layer,module,loss,samples,damp,time
0,mlp.up_proj,0.0000000180,0.01000,14.698
0,mlp.gate_proj,0.0000000209,0.01000,14.703
0,mlp.down_proj,0.0000000002,0.01000,19.578
1,mlp.up_proj,0.0000000474,0.01000,15.614
1,mlp.gate_proj,0.0000000532,0.01000,15.719
1,mlp.down_proj,0.0000000005,0.01000,19.688
2,mlp.gate_proj,0.0000000909,0.01000,13.516
2,mlp.up_proj,0.0000000763,0.01000,13.525
2,mlp.down_proj,0.0000000012,0.01000,16.068
3,mlp.up_proj,0.0000001099,0.01000,12.868
3,mlp.gate_proj,0.0000001215,0.01000,12.880
3,mlp.down_proj,0.0000000016,0.01000,18.303
4,mlp.gate_proj,0.0000001759,0.01000,13.142
4,mlp.up_proj,0.0000001591,0.01000,13.220
4,mlp.down_proj,0.0000000028,0.01000,18.932
5,mlp.up_proj,0.0000002053,0.01000,13.543
5,mlp.gate_proj,0.0000002436,0.01000,13.568
5,mlp.down_proj,0.0000000051,0.01000,19.012
6,mlp.gate_proj,0.0000003231,0.01000,13.359
6,mlp.up_proj,0.0000002633,0.01000,13.382
6,mlp.down_proj,0.0000000087,0.01000,18.271
7,mlp.up_proj,0.0000002905,0.01000,13.692
7,mlp.gate_proj,0.0000003638,0.01000,13.694
7,mlp.down_proj,0.0000000082,0.01000,19.970
8,mlp.gate_proj,0.0000003752,0.01000,14.667
8,mlp.up_proj,0.0000003103,0.01000,14.686
8,mlp.down_proj,0.0000000086,0.01000,19.069
9,mlp.gate_proj,0.0000003788,0.01000,15.200
9,mlp.up_proj,0.0000003346,0.01000,15.199
9,mlp.down_proj,0.0000000093,0.01000,19.630
10,mlp.gate_proj,0.0000003548,0.01000,15.047
10,mlp.up_proj,0.0000003430,0.01000,15.056
10,mlp.down_proj,0.0000000100,0.01000,19.467
11,mlp.up_proj,0.0000003548,0.01000,14.072
11,mlp.gate_proj,0.0000003465,0.01000,14.080
11,mlp.down_proj,0.0000000102,0.01000,20.719
12,mlp.gate_proj,0.0000003402,0.01000,14.767
12,mlp.up_proj,0.0000003621,0.01000,14.772
12,mlp.down_proj,0.0000000107,0.01000,19.933
13,mlp.up_proj,0.0000003774,0.01000,14.899
13,mlp.gate_proj,0.0000003481,0.01000,14.922
13,mlp.down_proj,0.0000000113,0.01000,19.574
14,mlp.up_proj,0.0000003916,0.01000,14.631
14,mlp.gate_proj,0.0000003426,0.01000,14.635
14,mlp.down_proj,0.0000000125,0.01000,18.898
15,mlp.up_proj,0.0000004363,0.01000,14.067
15,mlp.gate_proj,0.0000003765,0.01000,14.079
15,mlp.down_proj,0.0000000152,0.01000,20.600
16,mlp.up_proj,0.0000004807,0.01000,15.427
16,mlp.gate_proj,0.0000004186,0.01000,15.434
16,mlp.down_proj,0.0000000196,0.01000,20.297
17,mlp.up_proj,0.0000005352,0.01000,15.860
17,mlp.gate_proj,0.0000004607,0.01000,15.861
17,mlp.down_proj,0.0000000268,0.01000,20.976
18,mlp.gate_proj,0.0000005921,0.01000,14.978
18,mlp.up_proj,0.0000006467,0.01000,14.996
18,mlp.down_proj,0.0000000558,0.01000,20.681
19,mlp.up_proj,0.0000007851,0.01000,13.961
19,mlp.gate_proj,0.0000006913,0.01000,14.010
19,mlp.down_proj,0.0000000765,0.01000,21.126
20,mlp.gate_proj,0.0000009249,0.01000,15.675
20,mlp.up_proj,0.0000008837,0.01000,15.676
20,mlp.down_proj,0.0000000899,0.01000,21.957
21,mlp.up_proj,0.0000009645,0.01000,15.818
21,mlp.gate_proj,0.0000010762,0.01000,15.819
21,mlp.down_proj,0.0000001164,0.01000,21.863
22,mlp.up_proj,0.0000012398,0.01000,14.501
22,mlp.gate_proj,0.0000015297,0.01000,14.519
22,mlp.down_proj,0.0000001975,0.01000,20.517
23,mlp.up_proj,0.0000012593,0.01000,13.889
23,mlp.gate_proj,0.0000014170,0.01000,13.901
23,mlp.down_proj,0.0000001780,0.01000,21.184
24,mlp.gate_proj,0.0000015803,0.01000,15.295
24,mlp.up_proj,0.0000013320,0.01000,15.305
24,mlp.down_proj,0.0000001857,0.01000,22.126
25,mlp.gate_proj,0.0000018567,0.01000,14.848
25,mlp.up_proj,0.0000015032,0.01000,14.854
25,mlp.down_proj,0.0000001956,0.01000,22.090
26,mlp.gate_proj,0.0000021709,0.01000,14.203
26,mlp.up_proj,0.0000017253,0.01000,14.226
26,mlp.down_proj,0.0000002361,0.01000,20.607
27,mlp.up_proj,0.0000017307,0.01000,12.948
27,mlp.gate_proj,0.0000021289,0.01000,12.958
27,mlp.down_proj,0.0000002777,0.01000,20.681
28,mlp.up_proj,0.0000017948,0.01000,14.732
28,mlp.gate_proj,0.0000021849,0.01000,14.745
28,mlp.down_proj,0.0000003420,0.01000,21.724
29,mlp.gate_proj,0.0000023903,0.01000,14.719
29,mlp.up_proj,0.0000019471,0.01000,14.733
29,mlp.down_proj,0.0000004111,0.01000,21.808
30,mlp.gate_proj,0.0000022353,0.01000,14.004
30,mlp.up_proj,0.0000018579,0.01000,14.013
30,mlp.down_proj,0.0000005554,0.01000,19.780
31,mlp.up_proj,0.0000016236,0.01000,9.651
31,mlp.gate_proj,0.0000020070,0.01000,9.733
31,mlp.down_proj,0.0000009926,0.01000,8.318
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