Instructions to use VladHong/llama-cpp-K2-FAST2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use VladHong/llama-cpp-K2-FAST2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: llama cli -hf VladHong/llama-cpp-K2-FAST2
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: llama cli -hf VladHong/llama-cpp-K2-FAST2
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: ./build/bin/llama cli -hf VladHong/llama-cpp-K2-FAST2
Use Docker
docker model run hf.co/VladHong/llama-cpp-K2-FAST2
- Llama new
- LM Studio
- Jan
- Ollama
How to use VladHong/llama-cpp-K2-FAST2 with Ollama:
ollama run hf.co/VladHong/llama-cpp-K2-FAST2
- Unsloth Desktop
- Docker Model Runner
How to use VladHong/llama-cpp-K2-FAST2 with Docker Model Runner:
docker model run hf.co/VladHong/llama-cpp-K2-FAST2
- Lemonade
How to use VladHong/llama-cpp-K2-FAST2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull VladHong/llama-cpp-K2-FAST2
Run and chat with the model
lemonade run user.llama-cpp-K2-FAST2-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 5,982 Bytes
c59fd93 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | /**
* chatProcessingStore - Per-conversation processing state
*
* Owns the live processing snapshot shown while a conversation streams:
* token counts, tokens/sec, prompt progress. Updated from stream timings,
* restored from persisted message timings when a conversation loads.
*
* Composed under chatStore.processing; not exported from the stores barrel.
*/
import { MessageRole } from '$lib/enums';
// direct imports between stores, not via the barrel, to avoid circular deps
import { modelsStore } from '$lib/stores/models/index.svelte';
import { serverStore } from '$lib/stores/server.svelte';
import { settingsStore } from '$lib/stores/settings/index.svelte';
import type {
ApiProcessingState,
ChatMessagePromptProgress,
ChatMessageTimings,
DatabaseMessage
} from '$lib/types';
import { SvelteMap } from 'svelte/reactivity';
interface ProcessingTimingData {
cache_n: number;
predicted_n: number;
predicted_per_second: number;
prompt_ms?: number;
prompt_n: number;
prompt_progress?: ChatMessagePromptProgress;
}
export class ChatProcessingStore {
private _activeConversationId = $state<string | null>(null);
private states = new SvelteMap<string, ApiProcessingState>();
/** Processing state of the conversation currently shown in the UI. */
activeState = $derived(
this._activeConversationId ? (this.states.get(this._activeConversationId) ?? null) : null
);
get activeConversationId(): string | null {
return this._activeConversationId;
}
/**
* Applies a stream timings event (tokens/sec + token counts) to the given
* conversation's processing state. Shared by the chat and continue flows.
*/
applyStreamTimings(
timings?: ChatMessageTimings,
promptProgress?: ChatMessagePromptProgress,
conversationId?: string
): void {
const tokensPerSecond =
timings?.predicted_ms && timings?.predicted_n
? (timings.predicted_n / timings.predicted_ms) * 1000
: 0;
this.updateFromTimings(
{
cache_n: timings?.cache_n || 0,
predicted_n: timings?.predicted_n || 0,
predicted_per_second: tokensPerSecond,
prompt_ms: timings?.prompt_ms,
prompt_n: timings?.prompt_n || 0,
prompt_progress: promptProgress
},
conversationId
);
}
getConversationIds(): string[] {
return Array.from(this.states.keys());
}
getState(conversationId: string): ApiProcessingState | null {
return this.states.get(conversationId) ?? null;
}
restoreFromMessages(messages: DatabaseMessage[], conversationId: string): void {
for (let i = messages.length - 1; i >= 0; i--) {
const message = messages[i];
if (message.role === MessageRole.ASSISTANT && message.timings) {
this.setState(
conversationId,
this.parseTimingData({
cache_n: message.timings.cache_n || 0,
predicted_n: message.timings.predicted_n || 0,
predicted_per_second:
message.timings.predicted_n && message.timings.predicted_ms
? (message.timings.predicted_n / message.timings.predicted_ms) * 1000
: 0,
prompt_ms: message.timings.prompt_ms,
prompt_n: message.timings.prompt_n || 0
})
);
return;
}
}
}
setActiveConversation(conversationId: string | null): void {
this._activeConversationId = conversationId;
}
/** Passing null clears the state for the conversation. */
setState(conversationId: string, state: ApiProcessingState | null): void {
if (state === null) this.states.delete(conversationId);
else this.states.set(conversationId, state);
}
updateFromTimings(timingData: ProcessingTimingData, conversationId?: string): void {
const targetId = conversationId || this._activeConversationId;
if (targetId) {
this.setState(targetId, this.parseTimingData(timingData));
}
}
private getContextTotal(): number | null {
const activeConvId = this._activeConversationId;
const activeState = activeConvId ? this.getState(activeConvId) : null;
if (activeState && typeof activeState.contextTotal === 'number' && activeState.contextTotal > 0)
return activeState.contextTotal;
if (serverStore.isRouterMode) {
const modelContextSize = modelsStore.selectedModelContextSize;
if (typeof modelContextSize === 'number' && modelContextSize > 0) {
return modelContextSize;
}
} else {
const propsContextSize = serverStore.contextSize;
if (typeof propsContextSize === 'number' && propsContextSize > 0) {
return propsContextSize;
}
}
return null;
}
private parseTimingData(timingData: ProcessingTimingData): ApiProcessingState {
const cacheTokens = timingData.cache_n || 0,
predictedTokens = timingData.predicted_n || 0,
promptMs = timingData.prompt_ms || undefined,
promptTokens = timingData.prompt_n || 0,
tokensPerSecond = timingData.predicted_per_second || 0;
const promptProgress = timingData.prompt_progress;
const contextTotal = this.getContextTotal();
const currentConfig = settingsStore.config;
const outputTokensMax = currentConfig.max_tokens || -1;
const contextUsed = promptTokens + cacheTokens + predictedTokens,
outputTokensUsed = predictedTokens;
const progressCache = promptProgress?.cache || 0,
progressActualDone = (promptProgress?.processed ?? 0) - progressCache,
progressActualTotal = (promptProgress?.total ?? 0) - progressCache;
const progressPercent = promptProgress
? Math.round((progressActualDone / progressActualTotal) * 100)
: undefined;
return {
cacheTokens,
contextTotal,
contextUsed,
hasNextToken: predictedTokens > 0,
outputTokensMax,
outputTokensUsed,
progressPercent,
promptMs,
promptProgress,
promptTokens,
speculative: false,
status: predictedTokens > 0 ? 'generating' : promptProgress ? 'preparing' : 'idle',
temperature: currentConfig.temperature ?? 0.8,
tokensDecoded: predictedTokens,
tokensPerSecond,
tokensRemaining: outputTokensMax - predictedTokens,
topP: currentConfig.top_p ?? 0.95
};
}
}
export const chatProcessingStore = new ChatProcessingStore();
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