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
| <script lang="ts"> | |
| import { Button } from '$lib/components/ui/button'; | |
| import { InputWithSuggestions } from '$lib/components/app'; | |
| import { KeyboardKey } from '$lib/enums'; | |
| import { mcpStore } from '$lib/stores/mcp.svelte'; | |
| import { MIN_AUTOCOMPLETE_INPUT_LENGTH } from '$lib/constants'; | |
| import type { MCPResourceTemplateInfo } from '$lib/types'; | |
| import { | |
| debounce, | |
| extractTemplateVariables, | |
| expandTemplate, | |
| isTemplateComplete | |
| } from '$lib/utils'; | |
| interface Props { | |
| template: MCPResourceTemplateInfo; | |
| onResolve: (uri: string, serverName: string) => void; | |
| onCancel: () => void; | |
| } | |
| let { template, onResolve, onCancel }: Props = $props(); | |
| const variables = $derived(extractTemplateVariables(template.uriTemplate)); | |
| let values = $state<Record<string, string>>({}); | |
| let suggestions = $state<Record<string, string[]>>({}); | |
| let loadingSuggestions = $state<Record<string, boolean>>({}); | |
| let activeAutocomplete = $state<string | null>(null); | |
| let autocompleteIndex = $state(0); | |
| const expandedUri = $derived(expandTemplate(template.uriTemplate, values)); | |
| const isComplete = $derived(isTemplateComplete(template.uriTemplate, values)); | |
| const fetchCompletions = debounce(async (argName: string, value: string) => { | |
| if (value.length < 1) { | |
| suggestions[argName] = []; | |
| return; | |
| } | |
| loadingSuggestions[argName] = true; | |
| try { | |
| const result = await mcpStore.getResourceCompletions( | |
| template.serverName, | |
| template.uriTemplate, | |
| argName, | |
| value | |
| ); | |
| if (result && result.values.length > 0) { | |
| const filteredValues = result.values.filter((v) => v.trim() !== ''); | |
| if (filteredValues.length > 0) { | |
| suggestions[argName] = filteredValues; | |
| activeAutocomplete = argName; | |
| autocompleteIndex = 0; | |
| } else { | |
| suggestions[argName] = []; | |
| } | |
| } else { | |
| suggestions[argName] = []; | |
| } | |
| } catch (error) { | |
| console.error('[McpResourceTemplateForm] Failed to fetch completions:', error); | |
| suggestions[argName] = []; | |
| } finally { | |
| loadingSuggestions[argName] = false; | |
| } | |
| }, 200); | |
| function handleArgInput(argName: string, value: string) { | |
| values[argName] = value; | |
| fetchCompletions(argName, value); | |
| } | |
| function selectSuggestion(argName: string, value: string) { | |
| values[argName] = value; | |
| suggestions[argName] = []; | |
| activeAutocomplete = null; | |
| } | |
| function handleArgKeydown(event: KeyboardEvent, argName: string) { | |
| const argSuggestions = suggestions[argName] ?? []; | |
| if (event.key === KeyboardKey.ESCAPE) { | |
| event.preventDefault(); | |
| event.stopPropagation(); | |
| if (argSuggestions.length > 0 && activeAutocomplete === argName) { | |
| suggestions[argName] = []; | |
| activeAutocomplete = null; | |
| } else { | |
| onCancel(); | |
| } | |
| return; | |
| } | |
| if (argSuggestions.length === 0 || activeAutocomplete !== argName) return; | |
| if (event.key === KeyboardKey.ARROW_DOWN) { | |
| event.preventDefault(); | |
| autocompleteIndex = Math.min(autocompleteIndex + 1, argSuggestions.length - 1); | |
| } else if (event.key === KeyboardKey.ARROW_UP) { | |
| event.preventDefault(); | |
| autocompleteIndex = Math.max(autocompleteIndex - 1, 0); | |
| } else if (event.key === KeyboardKey.ENTER && argSuggestions[autocompleteIndex]) { | |
| event.preventDefault(); | |
| event.stopPropagation(); | |
| selectSuggestion(argName, argSuggestions[autocompleteIndex]); | |
| } | |
| } | |
| function handleArgBlur(argName: string) { | |
| setTimeout(() => { | |
| if (activeAutocomplete === argName) { | |
| suggestions[argName] = []; | |
| activeAutocomplete = null; | |
| } | |
| }, 150); | |
| } | |
| function handleArgFocus(argName: string) { | |
| const value = values[argName] ?? ''; | |
| if (value.length >= MIN_AUTOCOMPLETE_INPUT_LENGTH) { | |
| fetchCompletions(argName, value); | |
| } | |
| } | |
| function handleSubmit(event: SubmitEvent) { | |
| event.preventDefault(); | |
| if (isComplete) { | |
| onResolve(expandedUri, template.serverName); | |
| } | |
| } | |
| </script> | |
| <form onsubmit={handleSubmit} class="space-y-3"> | |
| {#each variables as variable (variable.name)} | |
| <InputWithSuggestions | |
| name={variable.name} | |
| value={values[variable.name] ?? ''} | |
| suggestions={suggestions[variable.name] ?? []} | |
| isLoadingSuggestions={loadingSuggestions[variable.name] ?? false} | |
| isAutocompleteActive={activeAutocomplete === variable.name} | |
| autocompleteIndex={activeAutocomplete === variable.name ? autocompleteIndex : 0} | |
| onInput={(value) => handleArgInput(variable.name, value)} | |
| onKeydown={(e) => handleArgKeydown(e, variable.name)} | |
| onBlur={() => handleArgBlur(variable.name)} | |
| onFocus={() => handleArgFocus(variable.name)} | |
| onSelectSuggestion={(value) => selectSuggestion(variable.name, value)} | |
| /> | |
| {/each} | |
| {#if isComplete} | |
| <div class="rounded-md bg-muted/50 px-3 py-2"> | |
| <p class="text-xs text-muted-foreground">Resolved URI:</p> | |
| <p class="mt-0.5 font-mono text-xs break-all">{expandedUri}</p> | |
| </div> | |
| {/if} | |
| <div class="flex justify-end gap-2 pt-1"> | |
| <Button type="button" size="sm" variant="secondary" onclick={onCancel}>Cancel</Button> | |
| <Button size="sm" type="submit" disabled={!isComplete}>Read Resource</Button> | |
| </div> | |
| </form> | |