Image-Text-to-Text
Safetensors
qwen4_exp
qwen3.8
qwen4-exp
awq
w4a16
fp8
e4m3
conversational
4-bit precision
Instructions to use leoncca/Qwen3.8-Flash-Next-AWQ-g32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use leoncca/Qwen3.8-Flash-Next-AWQ-g32 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "leoncca/Qwen3.8-Flash-Next-AWQ-g32" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "leoncca/Qwen3.8-Flash-Next-AWQ-g32", "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/leoncca/Qwen3.8-Flash-Next-AWQ-g32
- SGLang
How to use leoncca/Qwen3.8-Flash-Next-AWQ-g32 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 "leoncca/Qwen3.8-Flash-Next-AWQ-g32" \ --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": "leoncca/Qwen3.8-Flash-Next-AWQ-g32", "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 "leoncca/Qwen3.8-Flash-Next-AWQ-g32" \ --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": "leoncca/Qwen3.8-Flash-Next-AWQ-g32", "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 leoncca/Qwen3.8-Flash-Next-AWQ-g32 with Docker Model Runner:
docker model run hf.co/leoncca/Qwen3.8-Flash-Next-AWQ-g32
File size: 6,507 Bytes
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"artifact": {
"name": "Qwen3.8-Flash-Next-AWQ-g32",
"proposed_repository": "leoncca/Qwen3.8-Flash-Next-AWQ-g32",
"proposed_tag": "v1-formal-r6-da62ba7b",
"schema_version": 1
},
"sources": {
"base_model": {
"repository": "Qwen/Qwen3.8-Flash-Next",
"revision": "f5d08274bafd880402bd16f5e3e6c514136ec06c"
},
"ple": {
"repository": "Qwen/Qwen3.8-Flash-Next-FP8",
"revision": "bcd9f01ddc9cff2316eb84281bebcd5b058bddce"
},
"license": {
"filename": "LICENSE",
"name": "Qwen Community License 1.0",
"sha256": "a0dc422560841fd68e06d974907f8b4c709bca44a67daad2b528437bdf676c08"
}
},
"quantization": {
"method": "AWQ",
"weight_bits": 4,
"activation_dtype": "float16",
"group_size": 32,
"zero_point": true,
"checkpoint_format": "gemm",
"layout": "per-expert asymmetric routed-expert projections",
"quantized_projection_names": [
"gate_proj",
"up_proj",
"down_proj"
],
"protected": [
"vision",
"ple",
"attention",
"linear_attention",
"router",
"shared_expert",
"embeddings",
"lm_head",
"hyper_connections",
"mtp"
],
"quantizer": "gptqmodel:7.3.2",
"calibration_record_count": 684,
"calibration_corpus_sha256": "d2fbb16c5dcfaf26b713fd44d090cc8a3f2bbb31f1713f179e149f1f196b3dbb",
"processor_audit_sha256": "1ca4205795e83f3cf341cb3d8eb10ef7169bb4934a4435878af7bef8507a8c05",
"selective_bypass_selected_pairs": 93,
"selective_bypass_target_manifest_sha256": "9d4c2372bb065d2f9aadce457d98b0314c3844e1989e8fc6e36ce2a7088770c9",
"selective_bypass_source_report_sha256": "69d421dd1a57cf04ea55c533b20f042e9dc6a99e2b6f489c54aae3d2039b05a1",
"expert_coverage": {
"filename": "EXPERT_COVERAGE.json",
"natural_unique_expert_ids": "512/512",
"natural_layer_expert_pairs": "24568/24576",
"natural_layer_expert_coverage_fraction": 0.9996744791666666,
"fully_naturally_covered_layers": "45/48",
"zero_natural_token_pairs": 8,
"below_128_natural_token_pairs": 93,
"selective_augmentation_pairs": 93,
"runtime_zero_coverage_fallback_pairs": 0,
"native_route_matrix_sha256": "6c987b6d2a1506978f38bc0b4fa167d205250ba8f0629f05adddc880a26828b1"
},
"group_size_rationale": {
"expert_intermediate_width": 640,
"validated_tensor_parallel_size": 4,
"local_intermediate_width": 160,
"g32_divides_local_width": true,
"g128_divides_local_width": false,
"g128_runtime_effective_group_size": 32,
"native_g32_checkpoint_scale_and_zero_point_metadata_gib_approx": 8.79,
"g128_checkpoint_scale_and_zero_point_metadata_gib_approx": 2.20,
"native_g32_checkpoint_metadata_increase_gib_approx": 6.59,
"tp4_runtime_metadata_increase_vs_expanded_g128": false,
"decision": "Store native g32 so checkpoint groups align exactly with the TP4 expert partition and do not depend on load-time g128-to-g32 expansion.",
"quality_claim_boundary": "Native g32 was quality-validated; no matched full-checkpoint g32-versus-g128 quality comparison was performed."
}
},
"ple_splice": {
"dtype": "float8_e4m3fn",
"indexed_tensor_count": 129,
"indexed_payload_bytes": 51200245762,
"shard_count": 33,
"scale_value": 0.00019931793212890625,
"requantized": false
},
"checkpoint": {
"index_filename": "model.safetensors.index.json",
"base_awq_index_sha256": "da62ba7bfef57f8117f35fc78bf5daf14c0ab5eb1b8173b4d712c1f4d294819b",
"index_sha256": "f066a0154a9101b359c3a4d4fa6a83fb12b7126a9cf0f19cb772611d08cc07ee",
"indexed_tensor_count": 222771,
"indexed_payload_bytes": 137042968666,
"index_metadata_total_size": 137073585730,
"index_metadata_total_size_contract": "AWQ shard file bytes plus indexed PLE tensor bytes plus complete QSA scale shard bytes",
"physical_weight_shard_bytes": 138133206482,
"physical_model_shard_bytes": 138133209122,
"stored_tensor_count_in_all_selected_shards": 223827,
"unindexed_tensor_count_in_reused_ple_shards": 1056,
"awq_shard_count": 10,
"ple_shard_count": 33,
"qsa_kv_scale_shard_count": 1,
"qsa_kv_scale_filename": "model-kvscales.safetensors",
"qsa_kv_scale_tensor_count": 24,
"qsa_kv_scale_file_sha256": "c89b08bacde7d869342190b2f95046a83af37080241ae00ccae3a3d6888852fd",
"carrier_completion_sha256": "70fb852c73620edbf75858ab36c519005a5b4cec6597d2493c1355b4b901f077",
"host_side_final_audit_sha256": "b854f56010a564d7db9636c9977154f10c4ac2280f852899802ad7145cf85475",
"host_side_final_audit_status": "PASS"
},
"runtime_provenance": {
"fp16_kv_quality_source_commit": "c9af1be6e11011e398e8f89808e62d43fa453031",
"safetensors_index_assignment_commit": "1f8c110fb044a7ac3767e3b790d4efc936797dc9",
"awq_tp_group_alignment_commit": "ad4bfbec41b4ffb20877e2895f07957685d6438b",
"awq_tp_group_alignment_effect_for_g32_tp4": "inactive: effective group g32 and repeat factor 1",
"qsa_e4m3_core_pr": "https://github.com/1CatAI/1Cat-vLLM/pull/447",
"qsa_e4m3_core_commit": "58542ea69d43c6fc66242d218fbddbfdae7cd3b9",
"qsa_e4m3_bitcast_pr": "https://github.com/1CatAI/1Cat-vLLM/pull/452",
"qsa_e4m3_bitcast_commit": "6e42fb899767b4ac0769acaff9156314fb193d3a",
"qsa_e4m3_scale_hoist_pr": "https://github.com/1CatAI/1Cat-vLLM/pull/453",
"qsa_e4m3_scale_hoist_commit": "10a7bd0b36ff4684432b6e235013b7ad817a3534",
"combined_performance_test_commit": "526570c756e456a867aab86ea3ffd345195a082b",
"qwen4exp_multimodal_enablement_pr": "https://github.com/1CatAI/1Cat-vLLM/pull/458",
"qwen4exp_multimodal_enablement_commit": "dd68b9e8f44d248ad7082b871243a66c5fc1696a"
},
"publication": {
"internal_build_files_excluded": [
"final-checkpoint-audit.json",
"fp8-ple-splice.json",
"quant_log.csv",
"quantization-provenance.json"
],
"public_replacements": [
"MODEL_PROVENANCE.json",
"EXPERT_COVERAGE.json",
"VALIDATION.json",
"PUBLIC_RELEASE_AUDIT.json",
"SHA256SUMS"
],
"config_sanitization": {
"gpu_profile": "single-rtx-3090",
"offload_to_disk_path_removed": true,
"runtime_quantization_fields_changed": false
},
"weights_repacked": false,
"model_index_changed": true,
"model_index_change": "Added 24 checkpoint-bound QSA K/V scale assignments without changing AWQ or PLE weight payloads."
}
}
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