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.
- Libraries
- llama-cpp-python
How to use tda45/TdAI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tda45/TdAI", filename="llama.cpp/models/ggml-vocab-aquila.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- 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
- Atomic Chat new
- 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
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| 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} | |