modal.com on deployolom. full kontextus ablakkal maximum kim...

Prompt

modal.com on deployolom. full kontextus ablakkal maximum kimeneti tokennel es multi mediasan. Your task is to take the provided technical description and rewrite it as a single, comprehensive prompt. Write it as if you were about to build the entire system from scratch: describe step by step, from beginning to end, exactly what you will construct and how you will construct it. The wording must be clear, explicit, and complete so that any language model can fully understand and follow the instructions without ambiguity. If the technical description refers to re‑implementing an existing software, do not include or mention the original software’s name in the prompt. Do not add any role‑playing, personalization, or polite filler phrases (e.g., ‘You are a software developer’, ‘As an expert’, ‘Please kindly’). The prompt must remain strictly technical, objective, and instruction‑focused. Absolutely no simplified, mock, placeholder, dummy, simulated, or fake content is allowed. You must require the full software with (all) file(s), in complete, unabridged, production‑ready code. Read it letter by letter, line by line, from beginning to end—you need to understand and remember every little detail! Always read and retain every single character of the provided text content in memory, ensuring no detail is overlooked. Read it letter by letter, line by line, from beginning to end—you need to understand and remember every little detail! Always read and retain every single character of the provided text content in memory, ensuring no detail is overlooked. leheto lehjobb gpu beallitassal tehat leheto legolcsobb de leggyorsabb es legrovidebb cold start Solve this with the constraint that you cannot use the obvious solution. What is the most powerful, strongest approach? nem kell letolteni a fajlokat mert mar a modal fiokomba van letoltve a dealignai-qwen3-8-flash-next-abliterated-fp8/dealignai/Qwen3.8-Flash-Next-ABLITERATED-FP8 helyre a telkes model repo osszes fajl ja https://huggingface.co/dealignai/Qwen3.8-Flash-Next-ABLITERATED-FP8 Usage (vLLM) from vllm import LLM, SamplingParams llm = LLM(model="dealignai/Qwen3.8-Flash-Next-ABLITERATED-FP8", tensor_parallel_size=2, trust_remote_code=True) # reasoning via chat_template_kwargs: {"enable_thinking": True, "reasoning_effort": "xhigh"} # low | medium | xhigh PLE n-gram table is CPU-offloaded at runtime: set VLLM_PLE_CPU_OFFLOAD=1. MTP speculative decoding: speculative_config={"method": "qwen3_8_flash_next_mtp", "num_speculative_tokens": 1}. FP8 — serves on vLLM (Hopper / Blackwell; runs on 2× DGX Spark). {%- set image_count = namespace(value=0) -%} {%- set video_count = namespace(value=0) -%} {%- macro render_content(content, do_vision_count, is_system_content=false) -%} {%- if content is string -%} {{- content -}} {%- elif content is iterable and content is not mapping -%} {%- for item in content -%} {%- if "image" in item or "image_url" in item or item.type == "image" -%} {%- if is_system_content -%} {{- raise_exception("System message cannot contain images.") -}} {%- endif -%} {%- if do_vision_count -%} {%- set image_count.value = image_count.value + 1 -%} {%- endif -%} {%- if add_vision_id -%} {{- "Picture " ~ image_count.value ~ ": " -}} {%- endif -%} {{- "<|vision_start|><|image_pad|><|vision_end|>" -}} {%- elif "video" in item or item.type == "video" -%} {%- if is_system_content -%} {{- raise_exception("System message cannot contain videos.") -}} {%- endif -%} {%- if do_vision_count -%} {%- set video_count.value = video_count.value + 1 -%} {%- endif -%} {%- if add_vision_id -%} {{- "Video " ~ video_count.value ~ ": " -}} {%- endif -%} {{- "<|vision_start|><|video_pad|><|vision_end|>" -}} {%- elif "text" in item -%} {{- item.text -}} {%- else -%} {{- raise_exception("Unexpected item type in content.") -}} {%- endif -%} {%- endfor -%} {%- elif content is none or content is undefined -%} {{- "" -}} {%- else -%} {{- raise_exception("Unexpected content type.") -}} {%- endif -%} {%- endmacro -%} {%- if not messages -%} {{- raise_exception("No messages provided.") -}} {%- endif -%} {%- set reasoning_instructions = "" -%} {%- if enable_thinking is undefined or enable_thinking is true -%} {%- set resolved_reasoning_effort = reasoning_effort | default("xhigh") -%} {%- if resolved_reasoning_effort not in ("xhigh", "medium", "low") -%} {{- raise_exception("Unexpected reasoning effort " ~ reasoning_effort ~ ". Supported types are xhigh (default), medium, and low.") -}} {%- endif -%} {%- if resolved_reasoning_effort == "xhigh" -%} {%- set reasoning_instructions = "Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer." -%} {%- elif resolved_reasoning_effort == "low" -%} {%- set reasoning_instructions = "Reasoning effort is set to low. Keep your thinking brief and focused, moving directly to the conclusion without unnecessary elaboration." -%} {%- endif -%} {%- endif -%} {%- if tools and tools is iterable and tools is not mapping -%} {{- "<|im_start|>system\n" -}} {%- if reasoning_instructions -%} {{- reasoning_instructions + "\n\n" -}} {%- endif -%} {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" -}} {%- for tool in tools -%} {{- "\n" -}} {{- tool | tojson -}} {%- endfor -%} {{- "\n</tools>" -}} {{- "\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>" -}} {%- if messages[0].role == "system" -%} {%- set content = render_content(messages[0].content, false, true) | trim -%} {%- if content -%} {{- "\n\n" + content -}} {%- endif -%} {%- endif -%} {{- "<|im_end|>\n" -}} {%- elif messages[0].role == "system" -%} {%- set content = render_content(messages[0].content, false, true) | trim -%} {%- if content -%} {{- "<|im_start|>system\n" + (reasoning_instructions + "\n\n" if reasoning_instructions else "") + content + "<|im_end|>\n" -}} {%- elif reasoning_instructions -%} {{- "<|im_start|>system\n" + reasoning_instructions + "<|im_end|>\n" -}} {%- endif -%} {%- elif reasoning_instructions -%} {{- "<|im_start|>system\n" + reasoning_instructions + "<|im_end|>\n" -}} {%- endif -%} {%- set ns = namespace(multi_step_tool=true, last_query_index=messages | length - 1) -%} {%- for message in messages[::-1] -%} {%- set index = messages | length - 1 - loop.index0 -%} {%- if ns.multi_step_tool and message.role == "user" -%} {%- set content = render_content(message.content, false) | trim -%} {%- if not (content.startswith("<tool_response>") and content.endswith("</tool_response>")) -%} {%- set ns.multi_step_tool = false -%} {%- set ns.last_query_index = index -%} {%- endif -%} {%- endif -%} {%- endfor -%} {%- if ns.multi_step_tool -%} {{- raise_exception("No user query found in messages.") -}} {%- endif -%} {%- for message in messages -%} {%- set content = render_content(message.content, true) | trim -%} {%- if message.role == "system" -%} {%- if not loop.first -%} {{- raise_exception("System message must be at the beginning.") -}} {%- endif -%} {%- elif message.role == "user" -%} {{- "<|im_start|>" + message.role + "\n" + content + "<|im_end|>" + "\n" -}} {%- elif message.role == "assistant" -%} {%- set reasoning_content = "" -%} {%- if message.reasoning_content is string -%} {%- set reasoning_content = message.reasoning_content -%} {%- endif -%} {%- set reasoning_content = reasoning_content | trim -%} {%- if preserve_thinking is undefined or preserve_thinking is true or loop.index0 > ns.last_query_index -%} {{- "<|im_start|>" + message.role + "\n<think>\n" + reasoning_content + "\n</think>\n\n" + content -}} {%- else -%} {{- "<|im_start|>" + message.role + "\n" + content -}} {%- endif -%} {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping -%} {%- for tool_call in message.tool_calls -%} {%- if tool_call.function is defined -%} {%- set tool_call = tool_call.function -%} {%- endif -%} {%- if loop.first -%} {%- if content | trim -%} {{- "\n\n<tool_call>\n<function=" + tool_call.name + ">\n" -}} {%- else -%} {{- "<tool_call>\n<function=" + tool_call.name + ">\n" -}} {%- endif -%} {%- else -%} {{- "\n<tool_call>\n<function=" + tool_call.name + ">\n" -}} {%- endif -%} {%- if tool_call.arguments is defined and tool_call.arguments != "" -%} {%- for (args_name, args_value) in tool_call.arguments | items -%} {{- "<parameter=" + args_name + ">\n" -}} {%- set args_value = args_value | string if args_value is string else args_value | tojson | safe -%} {{- args_value -}} {{- "\n</parameter>\n" -}} {%- endfor -%} {%- endif -%} {{- "</function>\n</tool_call>" -}} {%- endfor -%} {%- endif -%} {{- "<|im_end|>\n" -}} {%- elif message.role == "tool" -%} {%- if loop.previtem and loop.previtem.role != "tool" -%} {{- "<|im_start|>user" -}} {%- endif -%} {{- "\n<tool_response>\n" -}} {{- content -}} {{- "\n</tool_response>" -}} {%- if not loop.last and loop.nextitem.role != "tool" -%} {{- "<|im_end|>\n" -}} {%- elif loop.last -%} {{- "<|im_end|>\n" -}} {%- endif -%} {%- else -%} {{- raise_exception("Unexpected message role.") -}} {%- endif -%} {%- endfor -%} {%- if add_generation_prompt -%} {{- "<|im_start|>assistant\n" -}} {%- if enable_thinking is defined and enable_thinking is false -%} {{- "<think>\n\n</think>\n\n" -}} {%- else -%} {{- "<think>\n" -}} {%- endif -%} {%- endif -%} nem akarok tobb gpu n mert szerintem teljesen feleslges es csak kurva dragara jon ki es olvastam hogy volt akik ilyen legolcsobb legkisebb gpu val is tudtak deployolni mondkuk ok a sima modet de nekem az uncensored kell de gondolom ugyanugy lehet azt is ilg deployololni https://github.com/ggml-org/llama.cpp.git The VRAM barrier is officially dead. I just ran Qwen 3.8 Flash Next (MoE) 125B A6B with a 250,000 context window on a single 24GB RTX 4090. 21 tokens/sec decode. 364 t/s prefill. no mtp. no dflash. no kv cache quantization! We are running datacenter models on consumer hardware. Tested on Ubuntu 22 | CUDA 13.0 | PCIe 4.0 x16 | 110 GB DDR4 System RAM with a continuous 28k prompt across all runs. ### The Benchmarks & Scaling # 1. Hybrid Offload (-ncmoe 40 @ 80k Context) Offloaded 40 expert layers to the GPU, pushing VRAM to the ceiling. ./build/bin/llama-server -m Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf -c 80000 --port 8080 -v --fit off -b 4096 -ub 4096 -ncmoe 40 Prefill: 383.85 t/s | Decode: 22.52 t/s Footprint: 23.85 GB VRAM | 97 GB RAM # 2. Full CPU MoE Offload (-cmoe @ 80k Context) Pinned all 512 expert layers to DDR4 RAM (-cmoe), keeping attention on the 4090. llama.cpp flags: (Same as above, replace -ncmoe 40 with -cmoe) Prefill: 355.72 t/s | Decode: 20.84 t/s Footprint: 11.66 GB VRAM (12GB+ VRAM freed up!) | 110 GB RAM # 3. The 180,000 Context Run Prefill: 357.75 t/s | Decode: 20.98 t/s | VRAM: 15.6 GB | RAM: 110 GB # 4. The 250,000 Context Absolute Ceiling ./build/bin/llama-server -m Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf -c 250000 --port 8080 -v --fit off -b 4096 -ub 4096 -cmoe Prefill: 364.29 t/s | Decode: 20.97 t/s Footprint: 18.3 GB VRAM (Still ~5.7 GB of VRAM headroom!) | 110 GB RAM ### Key Insights: -b 4096 -ub 4096: doubles the prompt ingestion from ~150 to 364+ t/s. -cmoe Free Lunch: Shifting expert layers to DDR4 RAM slashes VRAM from 24GB to 11.6GB with virtually zero decode penalty (22.5 -> 20.9 t/s), enabling the 250k context ceiling. Qwen 3.8 Flash-Next (UD-Q4_K_XL) is a massive 111.4 GB model split across 4 shards. To run this architecture, you must build from the experimental PR branch (#27742) by @danielhanchen: git clone && cd llama.cpp git fetch origin pull/27742/head:qwen-next && git checkout qwen-next cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=native -DBUILD_SHARED_LIBS=OFF cmake --build build --config Release -j $(nproc) --target llama-server A single 4090 paired with 100 GB of cheap DDR4 RAM will comfortably serve production grade 125B inference. While Qwen 3.8 27B (dense) still holds the crown for single 3090/4090 rigs, Flash Next proves 125B hybrid models are officially viable on consumer hardware. Hugging Face GGUF link and complete performance telemetry graphs are dropped in the replies below. GLM 5.3 Flash VS Qwen 3.8 Flash Next, which one takes the open weights crown this week?