shuvo-halder00

tinyllama-guanaco-trained

README

License: apache-2.0

Model Details

Developed by: [shuvo-halder00]
Model type: Causal Language Model (Fine-tuned adapter)
Language(s): English
License: Apache-2.0
Finetuned from model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

Uses

Direct Use

This model is intended for general conversational tasks, instruction following, and answering user queries based on the instruction fine-tuning dataset.

Limitations & Biases

The model is only 1.1B parameters, so it may occasionally generate incorrect facts (hallucinations).
It inherits the biases present in the base model and the Guanaco dataset.
Not recommended for critical production tasks (medical, legal, or high-stakes financial advice).

How to Get Started with the Model

Use the code below to load and run this model using 🤗 Transformers and PEFT:

python

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"adapter_id = "[shuvo-halder00]/[tinyllama-guanaco-trained]" # Load base modelmodel = AutoModelForCausalLM.from_pretrained(    base_model_id,    torch_dtype=torch.float16,    device_map="auto") # Load fine-tuned LoRA adaptermodel = PeftModel.from_pretrained(model, adapter_id) tokenizer = AutoTokenizer.from_pretrained(base_model_id) prompt = "### Human: What are the benefits of learning programming?\n### Assistant:"inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(    **inputs,     max_new_tokens=150,     temperature=0.7,     do_sample=True) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Model Details

Model Provider

shuvo-halder00

Model Tree

Base

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Adapter

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

Dedicated Endpoints

Explore FriendliAI today

Get started Talk to an engineer

README

License: apache-2.0

Model Details

Developed by: [shuvo-halder00]
Model type: Causal Language Model (Fine-tuned adapter)
Language(s): English
License: Apache-2.0
Finetuned from model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

Uses

Direct Use

This model is intended for general conversational tasks, instruction following, and answering user queries based on the instruction fine-tuning dataset.

Limitations & Biases

The model is only 1.1B parameters, so it may occasionally generate incorrect facts (hallucinations).
It inherits the biases present in the base model and the Guanaco dataset.
Not recommended for critical production tasks (medical, legal, or high-stakes financial advice).

How to Get Started with the Model

Use the code below to load and run this model using 🤗 Transformers and PEFT:

python

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"adapter_id = "[shuvo-halder00]/[tinyllama-guanaco-trained]" # Load base modelmodel = AutoModelForCausalLM.from_pretrained(    base_model_id,    torch_dtype=torch.float16,    device_map="auto") # Load fine-tuned LoRA adaptermodel = PeftModel.from_pretrained(model, adapter_id) tokenizer = AutoTokenizer.from_pretrained(base_model_id) prompt = "### Human: What are the benefits of learning programming?\n### Assistant:"inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(    **inputs,     max_new_tokens=150,     temperature=0.7,     do_sample=True) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"adapter_id = "[shuvo-halder00]/[tinyllama-guanaco-trained]" # Load base modelmodel = AutoModelForCausalLM.from_pretrained(    base_model_id,    torch_dtype=torch.float16,    device_map="auto") # Load fine-tuned LoRA adaptermodel = PeftModel.from_pretrained(model, adapter_id) tokenizer = AutoTokenizer.from_pretrained(base_model_id) prompt = "### Human: What are the benefits of learning programming?\n### Assistant:"inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(    **inputs,     max_new_tokens=150,     temperature=0.7,     do_sample=True) print(tokenizer.decode(outputs[0], skip_special_tokens=True))