Instructions to use Hinglish-Project/llama-3-8b-English-to-Hinglish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hinglish-Project/llama-3-8b-English-to-Hinglish with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Hinglish-Project/llama-3-8b-English-to-Hinglish")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Hinglish-Project/llama-3-8b-English-to-Hinglish")
model = AutoModelForCausalLM.from_pretrained("Hinglish-Project/llama-3-8b-English-to-Hinglish", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Hinglish-Project/llama-3-8b-English-to-Hinglish 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 Hinglish-Project/llama-3-8b-English-to-Hinglish:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Hinglish-Project/llama-3-8b-English-to-Hinglish:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Hinglish-Project/llama-3-8b-English-to-Hinglish:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Hinglish-Project/llama-3-8b-English-to-Hinglish: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 Hinglish-Project/llama-3-8b-English-to-Hinglish:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Hinglish-Project/llama-3-8b-English-to-Hinglish: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 Hinglish-Project/llama-3-8b-English-to-Hinglish:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Hinglish-Project/llama-3-8b-English-to-Hinglish:Q4_K_M

Use Docker

docker model run hf.co/Hinglish-Project/llama-3-8b-English-to-Hinglish:Q4_K_M

LM Studio
Jan

vLLM

How to use Hinglish-Project/llama-3-8b-English-to-Hinglish with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hinglish-Project/llama-3-8b-English-to-Hinglish"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hinglish-Project/llama-3-8b-English-to-Hinglish",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Hinglish-Project/llama-3-8b-English-to-Hinglish:Q4_K_M

SGLang

How to use Hinglish-Project/llama-3-8b-English-to-Hinglish with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Hinglish-Project/llama-3-8b-English-to-Hinglish" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hinglish-Project/llama-3-8b-English-to-Hinglish",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Hinglish-Project/llama-3-8b-English-to-Hinglish" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hinglish-Project/llama-3-8b-English-to-Hinglish",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Hinglish-Project/llama-3-8b-English-to-Hinglish with Ollama:
```
ollama run hf.co/Hinglish-Project/llama-3-8b-English-to-Hinglish:Q4_K_M
```

Unsloth Studio

How to use Hinglish-Project/llama-3-8b-English-to-Hinglish 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 Hinglish-Project/llama-3-8b-English-to-Hinglish 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 Hinglish-Project/llama-3-8b-English-to-Hinglish to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Hinglish-Project/llama-3-8b-English-to-Hinglish to start chatting

Docker Model Runner
How to use Hinglish-Project/llama-3-8b-English-to-Hinglish with Docker Model Runner:
```
docker model run hf.co/Hinglish-Project/llama-3-8b-English-to-Hinglish:Q4_K_M
```

Lemonade

How to use Hinglish-Project/llama-3-8b-English-to-Hinglish with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Hinglish-Project/llama-3-8b-English-to-Hinglish:Q4_K_M

Run and chat with the model

lemonade run user.llama-3-8b-English-to-Hinglish-Q4_K_M

List all available models

lemonade list

Atomic Chat

Inference:

!pip install -q "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install -q --no-deps "xformers<0.0.26" trl peft accelerate bitsandbytes

from unsloth import FastLanguageModel
import torch
max_seq_length = 512
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Hinglish-Project/llama-3-8b-English-to-Hinglish",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

def pipe(prompt):
  alpaca_prompt = """### Instrucion: Translate given text to Hinglish Text:

### Input:
{}

### Response:
"""

  inputs = tokenizer(
      [
          alpaca_prompt.format(prompt),
      ], return_tensors = "pt").to("cuda")

  outputs = model.generate(**inputs, max_new_tokens = 2048, use_cache = True)
  raw_text = tokenizer.batch_decode(outputs)[0]
  return raw_text.split("### Response:\n")[1].split("<|end_of_text|>")[0]

text = "This is a fine-tuned Hinglish translation model using Llama 3."
pipe(text)
## yeh ek fine-tuned Hinglish translation model hai jisme Llama 3 ka use kiya gaya hai.

Uploaded model

Developed by: Hinglish-Project
License: apache-2.0
Finetuned from model : unsloth/llama-8b-bnb-4bit

This Llama3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 245

Safetensors

Model size

8B params

Tensor type

F16

Model tree for Hinglish-Project/llama-3-8b-English-to-Hinglish

Base model

unsloth/llama-3-8b

Quantized

(33)

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Quantizations

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llama-3-8b-English-to-Hinglish

Inference:

Uploaded model

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