Instructions to use Pavankalyan/stages_0_1_mix_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pavankalyan/stages_0_1_mix_model with Transformers:

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

pipe = pipeline("text-generation", model="Pavankalyan/stages_0_1_mix_model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Pavankalyan/stages_0_1_mix_model")
model = AutoModelForCausalLM.from_pretrained("Pavankalyan/stages_0_1_mix_model")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Pavankalyan/stages_0_1_mix_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pavankalyan/stages_0_1_mix_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pavankalyan/stages_0_1_mix_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Pavankalyan/stages_0_1_mix_model

SGLang

How to use Pavankalyan/stages_0_1_mix_model 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 "Pavankalyan/stages_0_1_mix_model" \
    --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": "Pavankalyan/stages_0_1_mix_model",
		"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 "Pavankalyan/stages_0_1_mix_model" \
        --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": "Pavankalyan/stages_0_1_mix_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Pavankalyan/stages_0_1_mix_model with Docker Model Runner:
```
docker model run hf.co/Pavankalyan/stages_0_1_mix_model
```

Model Card for `Pavankalyan/stages_0_1_mix_model`

This is a chat model trained for multi-turn dialogue, using a format like:

<|user|>
What is the capital of France?
<|assistant|>
The capital of France is Paris.

How to Use the Model

You can use the model for chat-style interaction using the transformers library like so:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Pavankalyan/stages_0_1_mix_model"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

chat = "<|user|>
What is the capital of France?
<|assistant|>
"

inputs = tokenizer(chat, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=100,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    pad_token_id=tokenizer.eos_token_id,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 1

Safetensors

Model size

0.2B params

Tensor type

F32

Model Card for Pavankalyan/stages_0_1_mix_model

How to Use the Model

Model Card for `Pavankalyan/stages_0_1_mix_model`