Safetensors
GGUF
Turkish
llama
Llama-3
instruct
finetune
chatml
gpt4
synthetic data
distillation
function calling
json mode
axolotl
roleplaying
chat
Instructions to use tda45/TdAI 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 tda45/TdAI 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 tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./llama-cli -hf tda45/TdAI
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./build/bin/llama-cli -hf tda45/TdAI
Use Docker
docker model run hf.co/tda45/TdAI
- LM Studio
- Jan
- Ollama
How to use tda45/TdAI with Ollama:
ollama run hf.co/tda45/TdAI
- Unsloth Desktop
- Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
docker model run hf.co/tda45/TdAI
- Lemonade
How to use tda45/TdAI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tda45/TdAI
Run and chat with the model
lemonade run user.TdAI-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 2,062 Bytes
15c3607 | 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 | <script lang="ts">
import { buildConversationTree } from '$lib/stores/conversations.svelte';
import SidebarNavigationConversationItem from './SidebarNavigationConversationItem.svelte';
interface Props {
class?: string;
searchQuery: string;
filteredConversations: DatabaseConversation[];
currentChatId: string | undefined;
onSelect: (id: string) => void;
onEdit: (id: string) => void;
onDelete: (id: string) => void;
onStop: (id: string) => void;
}
let {
class: className = '',
searchQuery,
filteredConversations,
currentChatId,
onSelect,
onEdit,
onDelete,
onStop
}: Props = $props();
let tree = $derived(buildConversationTree(filteredConversations));
const hasQuery = $derived(searchQuery.trim().length > 0);
const showHeader = $derived(hasQuery && filteredConversations.length > 0);
const emptyMessage = $derived(hasQuery ? 'No results found' : 'Start typing to see results');
</script>
<div class="flex min-h-0 flex-1 flex-col gap-2 whitespace-nowrap {className}">
{#if showHeader}
<div
class="text-muted-foreground flex h-8 shrink-0 items-center rounded-md px-2 text-xs font-medium"
>
Search results
</div>
{/if}
<div class="min-h-0 flex-1 overflow-y-auto">
<ul class="flex w-full min-w-0 flex-col gap-1">
{#each tree as { conversation, depth } (conversation.id)}
<li class="group/item relative mb-1 p-0">
<SidebarNavigationConversationItem
conversation={{
id: conversation.id,
name: conversation.name,
lastModified: conversation.lastModified,
currNode: conversation.currNode,
forkedFromConversationId: conversation.forkedFromConversationId,
pinned: conversation.pinned
}}
{depth}
isActive={currentChatId === conversation.id}
{onSelect}
{onEdit}
{onDelete}
{onStop}
/>
</li>
{/each}
{#if tree.length === 0}
<li class="px-2 py-4 text-center">
<p class="mb-4 p-4 text-sm text-muted-foreground">
{emptyMessage}
</p>
</li>
{/if}
</ul>
</div>
</div>
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