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 Studio
How to use tda45/TdAI with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tda45/TdAI to start chatting
- 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: 2,758 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 78 79 80 81 82 83 | <script lang="ts">
import { ModelsService } from '$lib/services/models.service';
import { config } from '$lib/stores/settings.svelte';
import { TruncatedText } from '$lib/components/app';
interface Props {
modelId: string;
hideOrgName?: boolean;
showRaw?: boolean;
hideQuantization?: boolean;
hideTags?: boolean;
aliases?: string[];
tags?: string[];
class?: string;
}
let {
modelId,
hideOrgName = false,
showRaw = undefined,
hideQuantization,
hideTags,
aliases,
tags,
class: className = '',
...rest
}: Props = $props();
const badgeClass =
'inline-flex w-fit shrink-0 items-center justify-center whitespace-nowrap rounded-md border border-border/50 px-1 py-0 text-[10px] font-mono bg-foreground/15 dark:bg-foreground/10 text-foreground [a&]:hover:bg-foreground/25';
const tagBadgeClass =
'inline-flex w-fit shrink-0 items-center justify-center whitespace-nowrap rounded-md border border-border/50 px-1 py-0 text-[10px] font-mono text-foreground [a&]:hover:bg-accent [a&]:hover:text-accent-foreground';
let parsed = $derived(ModelsService.parseModelId(modelId));
let resolvedShowRaw = $derived(showRaw ?? (config().showRawModelNames as boolean) ?? false);
let resolvedHideQuantization = $derived(hideQuantization ?? !config().showModelQuantization);
let resolvedHideTags = $derived(hideTags ?? !config().showModelTags);
let uniqueAliases = $derived([...new Set(aliases ?? [])]);
let uniqueTags = $derived([...new Set([...(parsed.tags ?? []), ...(tags ?? [])])]);
let primaryAlias = $derived(uniqueAliases.length === 1 ? uniqueAliases[0] : null);
let displayName = $derived(primaryAlias ?? parsed.modelName ?? modelId);
</script>
{#if resolvedShowRaw}
<TruncatedText class="font-medium {className}" showTooltip={false} text={modelId} {...rest} />
{:else}
<span class="flex min-w-0 flex-wrap items-center gap-1 {className}" {...rest}>
<span class="min-w-0 truncate font-medium">
{#if !hideOrgName && parsed.orgName}{parsed.orgName}/{/if}{displayName}
</span>
{#if parsed.params}
<span class={badgeClass}>
{parsed.params}{parsed.activatedParams ? `-${parsed.activatedParams}` : ''}
</span>
{/if}
{#if parsed.quantization && !resolvedHideQuantization}
<span class={badgeClass}>
{parsed.quantization}
</span>
{/if}
{#if primaryAlias}
{#if primaryAlias !== parsed.modelName}
<span class={badgeClass}>{parsed.modelName ?? modelId}</span>
{/if}
{:else if uniqueAliases.length > 1}
{#each uniqueAliases as alias (alias)}
<span class={badgeClass}>{alias}</span>
{/each}
{/if}
{#if uniqueTags.length > 0 && !resolvedHideTags}
{#each uniqueTags as tag (tag)}
<span class={tagBadgeClass}>{tag}</span>
{/each}
{/if}
</span>
{/if}
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