from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ibm/granite-3.0-8b-instruct")
model = AutoModelForCausalLM.from_pretrained("ibm/granite-3.0-8b-instruct")
import { pipeline } from '@huggingface/transformers';
const generator = await pipeline('text-generation', 'ibm/granite-3.0-8b-instruct');
const output = await generator('Hello, I am a language model', { max_new_tokens: 50 });
// Using Hugging Face Inference API
import "github.com/hupe1980/go-huggingface"
client := huggingface.NewInferenceClient("HF_API_TOKEN")
ic, err := client.TextGeneration(context.Background(), &huggingface.TextGenerationRequest{
Model: "ibm/granite-3.0-8b-instruct",
Inputs: "Hello, I am a language model",
})
// Using HF candle crate
use candle_transformers::models::llama;
let api = Api::new()?;
let repo = api.model("ibm/granite-3.0-8b-instruct".to_string());
// Load model using llama.cpp
#include "llama.h"
llama_model_params model_params = llama_model_default_params();
llama_model* model = llama_model_load_from_file("ibm/granite-3.0-8b-instruct.gguf", model_params);
<?php
// Call HF Inference API for text generation
$ch = curl_init("https://api-inference.huggingface.co/models/ibm/granite-3.0-8b-instruct");
curl_setopt($ch, CURLOPT_HTTPHEADER, ["Authorization: Bearer HF_API_TOKEN"]);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode(["inputs" => "Hello, I am a language model"]));
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ")
model = AutoModelForCausalLM.from_pretrained("TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ")
import { pipeline } from '@huggingface/transformers';
const generator = await pipeline('text-generation', 'TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ');
const output = await generator('Hello, I am a language model', { max_new_tokens: 50 });
// Using Hugging Face Inference API
import "github.com/hupe1980/go-huggingface"
client := huggingface.NewInferenceClient("HF_API_TOKEN")
ic, err := client.TextGeneration(context.Background(), &huggingface.TextGenerationRequest{
Model: "TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ",
Inputs: "Hello, I am a language model",
})
// Using HF candle crate
use candle_transformers::models::llama;
let api = Api::new()?;
let repo = api.model("TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ".to_string());
// Load model using llama.cpp
#include "llama.h"
llama_model_params model_params = llama_model_default_params();
llama_model* model = llama_model_load_from_file("TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ.gguf", model_params);
<?php
// Call HF Inference API for text generation
$ch = curl_init("https://api-inference.huggingface.co/models/TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ");
curl_setopt($ch, CURLOPT_HTTPHEADER, ["Authorization: Bearer HF_API_TOKEN"]);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode(["inputs" => "Hello, I am a language model"]));
