Image-Text-to-Text
Transformers
Safetensors
kimi_k25
feature-extraction
kimi
fp4
nvfp4
vllm
llm-compressor
compressed-tensors
conversational
custom_code
Instructions to use RedHatAI/Kimi-K2.6-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/Kimi-K2.6-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="RedHatAI/Kimi-K2.6-NVFP4", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("RedHatAI/Kimi-K2.6-NVFP4", trust_remote_code=True) model = AutoModel.from_pretrained("RedHatAI/Kimi-K2.6-NVFP4", trust_remote_code=True, device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RedHatAI/Kimi-K2.6-NVFP4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RedHatAI/Kimi-K2.6-NVFP4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RedHatAI/Kimi-K2.6-NVFP4", "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/RedHatAI/Kimi-K2.6-NVFP4
- SGLang
How to use RedHatAI/Kimi-K2.6-NVFP4 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 "RedHatAI/Kimi-K2.6-NVFP4" \ --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": "RedHatAI/Kimi-K2.6-NVFP4", "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 "RedHatAI/Kimi-K2.6-NVFP4" \ --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": "RedHatAI/Kimi-K2.6-NVFP4", "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 RedHatAI/Kimi-K2.6-NVFP4 with Docker Model Runner:
docker model run hf.co/RedHatAI/Kimi-K2.6-NVFP4
Update SWE-Bench Lite eval results (official harness resolved-rate + submission-rate, vLLM 0.24.0)
Browse files
every_eval_ever/swebench_lite_dev.json
CHANGED
|
@@ -1,19 +1,19 @@
|
|
| 1 |
{
|
| 2 |
"schema_version": "0.2.2",
|
| 3 |
-
"evaluation_id": "swebench_lite_dev/RedHatAI/Kimi-K2.6-NVFP4/
|
| 4 |
-
"retrieved_timestamp": "
|
| 5 |
"source_metadata": {
|
| 6 |
-
"source_name": "
|
| 7 |
"source_type": "evaluation_run",
|
| 8 |
"source_organization_name": "RedHatAI",
|
| 9 |
-
"evaluator_relationship": "
|
| 10 |
"additional_details": {
|
| 11 |
-
"note": "
|
| 12 |
}
|
| 13 |
},
|
| 14 |
"eval_library": {
|
| 15 |
-
"name": "
|
| 16 |
-
"version": "
|
| 17 |
},
|
| 18 |
"model_info": {
|
| 19 |
"name": "RedHatAI/Kimi-K2.6-NVFP4",
|
|
@@ -21,7 +21,7 @@
|
|
| 21 |
"developer": "RedHatAI",
|
| 22 |
"inference_engine": {
|
| 23 |
"name": "vllm",
|
| 24 |
-
"version": "0.
|
| 25 |
},
|
| 26 |
"additional_details": {
|
| 27 |
"profile": "kimi_nvfp4",
|
|
@@ -31,14 +31,33 @@
|
|
| 31 |
},
|
| 32 |
"evaluation_results": [
|
| 33 |
{
|
| 34 |
-
"evaluation_name": "swebench_lite_dev/
|
| 35 |
"source_data": {
|
| 36 |
"dataset_name": "SWE-bench Lite dev",
|
| 37 |
"source_type": "hf_dataset",
|
| 38 |
"hf_repo": "princeton-nlp/SWE-Bench_Lite",
|
| 39 |
"hf_split": "dev",
|
| 40 |
-
"samples_number": 23
|
| 41 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 42 |
"marshmallow-code__marshmallow-1343",
|
| 43 |
"marshmallow-code__marshmallow-1359",
|
| 44 |
"pvlib__pvlib-python-1072",
|
|
@@ -53,35 +72,13 @@
|
|
| 53 |
"pydicom__pydicom-901",
|
| 54 |
"pylint-dev__astroid-1196",
|
| 55 |
"pylint-dev__astroid-1268",
|
| 56 |
-
"pylint-dev__astroid-1333",
|
| 57 |
"pylint-dev__astroid-1866",
|
| 58 |
"pylint-dev__astroid-1978",
|
| 59 |
"pyvista__pyvista-4315",
|
| 60 |
"sqlfluff__sqlfluff-1517",
|
| 61 |
-
"sqlfluff__sqlfluff-1625",
|
| 62 |
-
"sqlfluff__sqlfluff-1733",
|
| 63 |
-
"sqlfluff__sqlfluff-1763",
|
| 64 |
"sqlfluff__sqlfluff-2419"
|
| 65 |
-
]
|
| 66 |
-
|
| 67 |
-
"metric_config": {
|
| 68 |
-
"evaluation_description": "Submission rate (Submitted / total instances).",
|
| 69 |
-
"lower_is_better": false,
|
| 70 |
-
"score_type": "continuous",
|
| 71 |
-
"min_score": 0.0,
|
| 72 |
-
"max_score": 1.0
|
| 73 |
-
},
|
| 74 |
-
"score_details": {
|
| 75 |
-
"score": 0.9130434782608695,
|
| 76 |
-
"details": {
|
| 77 |
-
"total_instances": "23",
|
| 78 |
-
"submitted_instances": "21",
|
| 79 |
-
"unsubmitted_instances": "2",
|
| 80 |
-
"status_counts": "{\"RepeatedFormatError\": 2, \"Submitted\": 21}",
|
| 81 |
-
"unsubmitted_ids": "[\"sqlfluff__sqlfluff-1625\", \"sqlfluff__sqlfluff-1733\"]",
|
| 82 |
-
"source_exit_status_files": "[\"/home/shubhra/kimik2.6_evals/runs/swebench/20260629T171225Z_kimi_nvfp4_lite_dev/output/exit_statuses_1782760165.6381476.yaml\", \"/home/shubhra/kimik2.6_evals/runs/swebench/20260630T135435Z_kimi_nvfp4_lite_dev/output/exit_statuses_1782827902.5985954.yaml\", \"/home/shubhra/kimik2.6_evals/runs/swebench/20260630T141107Z_kimi_nvfp4_lite_dev/output/exit_statuses_1782830121.1086848.yaml\", \"/home/shubhra/kimik2.6_evals/runs/swebench/20260630T151900Z_kimi_nvfp4_lite_dev/output/exit_statuses_1782833877.9161725.yaml\", \"/home/shubhra/kimik2.6_evals/runs/swebench/20260630T190648Z_kimi_nvfp4_lite_dev/output/exit_statuses_1782846629.8118455.yaml\", \"/home/shubhra/kimik2.6_evals/runs/swebench/20260630T192452Z_kimi_nvfp4_lite_dev/output/exit_statuses_1782848479.344266.yaml\", \"/home/shubhra/kimik2.6_evals/runs/swebench/20260630T195651Z_kimi_nvfp4_lite_dev/output/exit_statuses_1782850223.0775344.yaml\"]"
|
| 83 |
-
},
|
| 84 |
-
"completed_ids": [
|
| 85 |
"marshmallow-code__marshmallow-1343",
|
| 86 |
"marshmallow-code__marshmallow-1359",
|
| 87 |
"pvlib__pvlib-python-1072",
|
|
@@ -106,6 +103,50 @@
|
|
| 106 |
"sqlfluff__sqlfluff-1763",
|
| 107 |
"sqlfluff__sqlfluff-2419"
|
| 108 |
],
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 109 |
"submitted_ids": [
|
| 110 |
"marshmallow-code__marshmallow-1343",
|
| 111 |
"marshmallow-code__marshmallow-1359",
|
|
@@ -126,6 +167,8 @@
|
|
| 126 |
"pylint-dev__astroid-1978",
|
| 127 |
"pyvista__pyvista-4315",
|
| 128 |
"sqlfluff__sqlfluff-1517",
|
|
|
|
|
|
|
| 129 |
"sqlfluff__sqlfluff-1763",
|
| 130 |
"sqlfluff__sqlfluff-2419"
|
| 131 |
]
|
|
@@ -144,4 +187,4 @@
|
|
| 144 |
}
|
| 145 |
}
|
| 146 |
]
|
| 147 |
-
}
|
|
|
|
| 1 |
{
|
| 2 |
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "swebench_lite_dev/RedHatAI/Kimi-K2.6-NVFP4/1784247001.8595154",
|
| 4 |
+
"retrieved_timestamp": "1784247001.8595154",
|
| 5 |
"source_metadata": {
|
| 6 |
+
"source_name": "SWE-bench harness",
|
| 7 |
"source_type": "evaluation_run",
|
| 8 |
"source_organization_name": "RedHatAI",
|
| 9 |
+
"evaluator_relationship": "first_party",
|
| 10 |
"additional_details": {
|
| 11 |
+
"note": "predictions generated with mini-swe-agent, resolved-rate scored with the official SWE-bench harness (swebench.harness.run_evaluation)"
|
| 12 |
}
|
| 13 |
},
|
| 14 |
"eval_library": {
|
| 15 |
+
"name": "swebench",
|
| 16 |
+
"version": "4.1.0"
|
| 17 |
},
|
| 18 |
"model_info": {
|
| 19 |
"name": "RedHatAI/Kimi-K2.6-NVFP4",
|
|
|
|
| 21 |
"developer": "RedHatAI",
|
| 22 |
"inference_engine": {
|
| 23 |
"name": "vllm",
|
| 24 |
+
"version": "0.24.0"
|
| 25 |
},
|
| 26 |
"additional_details": {
|
| 27 |
"profile": "kimi_nvfp4",
|
|
|
|
| 31 |
},
|
| 32 |
"evaluation_results": [
|
| 33 |
{
|
| 34 |
+
"evaluation_name": "swebench_lite_dev/resolved_rate",
|
| 35 |
"source_data": {
|
| 36 |
"dataset_name": "SWE-bench Lite dev",
|
| 37 |
"source_type": "hf_dataset",
|
| 38 |
"hf_repo": "princeton-nlp/SWE-Bench_Lite",
|
| 39 |
"hf_split": "dev",
|
| 40 |
+
"samples_number": 23
|
| 41 |
+
},
|
| 42 |
+
"metric_config": {
|
| 43 |
+
"evaluation_description": "Resolve rate: fraction of benchmark instances where the submitted patch passes verification (0.0-1.0), scored with the official SWE-bench harness.",
|
| 44 |
+
"lower_is_better": false,
|
| 45 |
+
"score_type": "continuous",
|
| 46 |
+
"min_score": 0.0,
|
| 47 |
+
"max_score": 1.0
|
| 48 |
+
},
|
| 49 |
+
"score_details": {
|
| 50 |
+
"score": 0.21739130434782608,
|
| 51 |
+
"details": {
|
| 52 |
+
"total_instances": "23",
|
| 53 |
+
"resolved_instances": "5",
|
| 54 |
+
"submitted_instances": "23",
|
| 55 |
+
"completed_instances": "19",
|
| 56 |
+
"unresolved_instances": "14",
|
| 57 |
+
"empty_patch_instances": "4",
|
| 58 |
+
"error_instances": "0"
|
| 59 |
+
},
|
| 60 |
+
"completed_ids": [
|
| 61 |
"marshmallow-code__marshmallow-1343",
|
| 62 |
"marshmallow-code__marshmallow-1359",
|
| 63 |
"pvlib__pvlib-python-1072",
|
|
|
|
| 72 |
"pydicom__pydicom-901",
|
| 73 |
"pylint-dev__astroid-1196",
|
| 74 |
"pylint-dev__astroid-1268",
|
|
|
|
| 75 |
"pylint-dev__astroid-1866",
|
| 76 |
"pylint-dev__astroid-1978",
|
| 77 |
"pyvista__pyvista-4315",
|
| 78 |
"sqlfluff__sqlfluff-1517",
|
|
|
|
|
|
|
|
|
|
| 79 |
"sqlfluff__sqlfluff-2419"
|
| 80 |
+
],
|
| 81 |
+
"submitted_ids": [
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 82 |
"marshmallow-code__marshmallow-1343",
|
| 83 |
"marshmallow-code__marshmallow-1359",
|
| 84 |
"pvlib__pvlib-python-1072",
|
|
|
|
| 103 |
"sqlfluff__sqlfluff-1763",
|
| 104 |
"sqlfluff__sqlfluff-2419"
|
| 105 |
],
|
| 106 |
+
"resolved_ids": [
|
| 107 |
+
"marshmallow-code__marshmallow-1343",
|
| 108 |
+
"pydicom__pydicom-1256",
|
| 109 |
+
"pydicom__pydicom-1694",
|
| 110 |
+
"pylint-dev__astroid-1268",
|
| 111 |
+
"sqlfluff__sqlfluff-2419"
|
| 112 |
+
]
|
| 113 |
+
},
|
| 114 |
+
"generation_config": {
|
| 115 |
+
"generation_args": {
|
| 116 |
+
"agentic_eval_config": {
|
| 117 |
+
"available_tools": [
|
| 118 |
+
{
|
| 119 |
+
"name": "bash"
|
| 120 |
+
}
|
| 121 |
+
]
|
| 122 |
+
},
|
| 123 |
+
"max_attempts": 1
|
| 124 |
+
}
|
| 125 |
+
}
|
| 126 |
+
},
|
| 127 |
+
{
|
| 128 |
+
"evaluation_name": "swebench_lite_dev/submission_rate",
|
| 129 |
+
"source_data": {
|
| 130 |
+
"dataset_name": "SWE-bench Lite dev",
|
| 131 |
+
"source_type": "hf_dataset",
|
| 132 |
+
"hf_repo": "princeton-nlp/SWE-Bench_Lite",
|
| 133 |
+
"hf_split": "dev",
|
| 134 |
+
"samples_number": 23
|
| 135 |
+
},
|
| 136 |
+
"metric_config": {
|
| 137 |
+
"evaluation_description": "Submission rate (Submitted / total instances).",
|
| 138 |
+
"lower_is_better": false,
|
| 139 |
+
"score_type": "continuous",
|
| 140 |
+
"min_score": 0.0,
|
| 141 |
+
"max_score": 1.0
|
| 142 |
+
},
|
| 143 |
+
"score_details": {
|
| 144 |
+
"score": 1.0,
|
| 145 |
+
"details": {
|
| 146 |
+
"total_instances": "23",
|
| 147 |
+
"submitted_instances": "23",
|
| 148 |
+
"unsubmitted_instances": "0"
|
| 149 |
+
},
|
| 150 |
"submitted_ids": [
|
| 151 |
"marshmallow-code__marshmallow-1343",
|
| 152 |
"marshmallow-code__marshmallow-1359",
|
|
|
|
| 167 |
"pylint-dev__astroid-1978",
|
| 168 |
"pyvista__pyvista-4315",
|
| 169 |
"sqlfluff__sqlfluff-1517",
|
| 170 |
+
"sqlfluff__sqlfluff-1625",
|
| 171 |
+
"sqlfluff__sqlfluff-1733",
|
| 172 |
"sqlfluff__sqlfluff-1763",
|
| 173 |
"sqlfluff__sqlfluff-2419"
|
| 174 |
]
|
|
|
|
| 187 |
}
|
| 188 |
}
|
| 189 |
]
|
| 190 |
+
}
|