Instructions to use AJKADZ/PHI_CODER 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 AJKADZ/PHI_CODER 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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: llama cli -hf AJKADZ/PHI_CODER:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: llama cli -hf AJKADZ/PHI_CODER:Q4_K_M
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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AJKADZ/PHI_CODER:Q4_K_M
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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AJKADZ/PHI_CODER:Q4_K_M
Use Docker
docker model run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use AJKADZ/PHI_CODER with Ollama:
ollama run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use AJKADZ/PHI_CODER with Docker Model Runner:
docker model run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- Lemonade
How to use AJKADZ/PHI_CODER with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AJKADZ/PHI_CODER:Q4_K_M
Run and chat with the model
lemonade run user.PHI_CODER-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download phi-coder-hf/llama.cpp/tools/server/public_legacy/completion.js from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 6.29 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/tools/server/public_legacy/completion.js
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/tools/server/public_legacy/completion.js
-
curl -L -o completion.js https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/tools/server/public_legacy/completion.js
6.29 kB
| const paramDefaults = { | |
| stream: true, | |
| n_predict: 500, | |
| temperature: 0.2, | |
| stop: ["</s>"] | |
| }; | |
| let generation_settings = null; | |
| // Completes the prompt as a generator. Recommended for most use cases. | |
| // | |
| // Example: | |
| // | |
| // import { llama } from '/completion.js' | |
| // | |
| // const request = llama("Tell me a joke", {n_predict: 800}) | |
| // for await (const chunk of request) { | |
| // document.write(chunk.data.content) | |
| // } | |
| // | |
| export async function* llama(prompt, params = {}, config = {}) { | |
| let controller = config.controller; | |
| const api_url = config.api_url?.replace(/\/+$/, '') || ""; | |
| if (!controller) { | |
| controller = new AbortController(); | |
| } | |
| const completionParams = { ...paramDefaults, ...params, prompt }; | |
| const response = await fetch(`${api_url}${config.endpoint || '/completion'}`, { | |
| method: 'POST', | |
| body: JSON.stringify(completionParams), | |
| headers: { | |
| 'Connection': 'keep-alive', | |
| 'Content-Type': 'application/json', | |
| 'Accept': 'text/event-stream', | |
| ...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {}) | |
| }, | |
| signal: controller.signal, | |
| }); | |
| const reader = response.body.getReader(); | |
| const decoder = new TextDecoder(); | |
| let content = ""; | |
| let leftover = ""; // Buffer for partially read lines | |
| try { | |
| let cont = true; | |
| while (cont) { | |
| const result = await reader.read(); | |
| if (result.done) { | |
| break; | |
| } | |
| // Add any leftover data to the current chunk of data | |
| const text = leftover + decoder.decode(result.value); | |
| // Check if the last character is a line break | |
| const endsWithLineBreak = text.endsWith('\n'); | |
| // Split the text into lines | |
| let lines = text.split('\n'); | |
| // If the text doesn't end with a line break, then the last line is incomplete | |
| // Store it in leftover to be added to the next chunk of data | |
| if (!endsWithLineBreak) { | |
| leftover = lines.pop(); | |
| } else { | |
| leftover = ""; // Reset leftover if we have a line break at the end | |
| } | |
| // Parse all sse events and add them to result | |
| const regex = /^(\S+):\s(.*)$/gm; | |
| for (const line of lines) { | |
| const match = regex.exec(line); | |
| if (match) { | |
| result[match[1]] = match[2]; | |
| if (result.data === '[DONE]') { | |
| cont = false; | |
| break; | |
| } | |
| // since we know this is llama.cpp, let's just decode the json in data | |
| if (result.data) { | |
| result.data = JSON.parse(result.data); | |
| content += result.data.content; | |
| // yield | |
| yield result; | |
| // if we got a stop token from server, we will break here | |
| if (result.data.stop) { | |
| if (result.data.generation_settings) { | |
| generation_settings = result.data.generation_settings; | |
| } | |
| cont = false; | |
| break; | |
| } | |
| } | |
| if (result.error) { | |
| try { | |
| result.error = JSON.parse(result.error); | |
| if (result.error.message.includes('slot unavailable')) { | |
| // Throw an error to be caught by upstream callers | |
| throw new Error('slot unavailable'); | |
| } else { | |
| console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`); | |
| } | |
| } catch(e) { | |
| console.error(`llama.cpp error ${result.error}`) | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } catch (e) { | |
| if (e.name !== 'AbortError') { | |
| console.error("llama error: ", e); | |
| } | |
| throw e; | |
| } | |
| finally { | |
| controller.abort(); | |
| } | |
| return content; | |
| } | |
| // Call llama, return an event target that you can subscribe to | |
| // | |
| // Example: | |
| // | |
| // import { llamaEventTarget } from '/completion.js' | |
| // | |
| // const conn = llamaEventTarget(prompt) | |
| // conn.addEventListener("message", (chunk) => { | |
| // document.write(chunk.detail.content) | |
| // }) | |
| // | |
| export const llamaEventTarget = (prompt, params = {}, config = {}) => { | |
| const eventTarget = new EventTarget(); | |
| (async () => { | |
| let content = ""; | |
| for await (const chunk of llama(prompt, params, config)) { | |
| if (chunk.data) { | |
| content += chunk.data.content; | |
| eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data })); | |
| } | |
| if (chunk.data.generation_settings) { | |
| eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings })); | |
| } | |
| if (chunk.data.timings) { | |
| eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings })); | |
| } | |
| } | |
| eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } })); | |
| })(); | |
| return eventTarget; | |
| } | |
| // Call llama, return a promise that resolves to the completed text. This does not support streaming | |
| // | |
| // Example: | |
| // | |
| // llamaPromise(prompt).then((content) => { | |
| // document.write(content) | |
| // }) | |
| // | |
| // or | |
| // | |
| // const content = await llamaPromise(prompt) | |
| // document.write(content) | |
| // | |
| export const llamaPromise = (prompt, params = {}, config = {}) => { | |
| return new Promise(async (resolve, reject) => { | |
| let content = ""; | |
| try { | |
| for await (const chunk of llama(prompt, params, config)) { | |
| content += chunk.data.content; | |
| } | |
| resolve(content); | |
| } catch (error) { | |
| reject(error); | |
| } | |
| }); | |
| }; | |
| /** | |
| * (deprecated) | |
| */ | |
| export const llamaComplete = async (params, controller, callback) => { | |
| for await (const chunk of llama(params.prompt, params, { controller })) { | |
| callback(chunk); | |
| } | |
| } | |
| // Get the model info from the server. This is useful for getting the context window and so on. | |
| export const llamaModelInfo = async (config = {}) => { | |
| if (!generation_settings) { | |
| const api_url = config.api_url?.replace(/\/+$/, '') || ""; | |
| const props = await fetch(`${api_url}/props`).then(r => r.json()); | |
| generation_settings = props.default_generation_settings; | |
| } | |
| return generation_settings; | |
| } | |