Instructions to use Pinkstack/PGAM-WIT-Conversational-3B-PyTorch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pinkstack/PGAM-WIT-Conversational-3B-PyTorch with Transformers:

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

pipe = pipeline("text-generation", model="Pinkstack/PGAM-WIT-Conversational-3B-PyTorch")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Pinkstack/PGAM-WIT-Conversational-3B-PyTorch")
model = AutoModelForCausalLM.from_pretrained("Pinkstack/PGAM-WIT-Conversational-3B-PyTorch")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Pinkstack/PGAM-WIT-Conversational-3B-PyTorch with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pinkstack/PGAM-WIT-Conversational-3B-PyTorch"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pinkstack/PGAM-WIT-Conversational-3B-PyTorch",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pinkstack/PGAM-WIT-Conversational-3B-PyTorch

SGLang

How to use Pinkstack/PGAM-WIT-Conversational-3B-PyTorch 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 "Pinkstack/PGAM-WIT-Conversational-3B-PyTorch" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pinkstack/PGAM-WIT-Conversational-3B-PyTorch",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Pinkstack/PGAM-WIT-Conversational-3B-PyTorch" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pinkstack/PGAM-WIT-Conversational-3B-PyTorch",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Pinkstack/PGAM-WIT-Conversational-3B-PyTorch 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 Pinkstack/PGAM-WIT-Conversational-3B-PyTorch 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 Pinkstack/PGAM-WIT-Conversational-3B-PyTorch to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Pinkstack/PGAM-WIT-Conversational-3B-PyTorch to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Pinkstack/PGAM-WIT-Conversational-3B-PyTorch",
    max_seq_length=2048,
)

Docker Model Runner
How to use Pinkstack/PGAM-WIT-Conversational-3B-PyTorch with Docker Model Runner:
```
docker model run hf.co/Pinkstack/PGAM-WIT-Conversational-3B-PyTorch
```

PGAM-WIT-Conversational-3B-PyTorch / tokenizer_config.json

Pinkstack

Upload tokenizer

f457a7a verified over 1 year ago

raw

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	{
	"added_tokens_decoder": {
	"151643": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
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	"single_word": false,
	"special": true
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	"151663": {
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	"151664": {
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	"151665": {
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	"single_word": false,
	"special": true
	}
	},
	"bos_token": null,
	"chat_template": "{% if 'role' in messages[0] %}{% for message in messages %}{% if message['role'] == 'user' %}{{'<\|im_start\|>user\n' + message['content'] + '<\|im_end\|>\n'}}{% elif message['role'] == 'assistant' %}{{'<\|im_start\|>assistant\n' + message['content'] + '<\|im_end\|>\n' }}{% else %}{{ '<\|im_start\|>system\n' + message['content'] + '<\|im_end\|>\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<\|im_start\|>assistant\n' }}{% endif %}{% else %}{% for message in messages %}{% if message['from'] == 'human' %}{{'<\|im_start\|>user\n' + message['value'] + '<\|im_end\|>\n'}}{% elif message['from'] == 'gpt' %}{{'<\|im_start\|>assistant\n' + message['value'] + '<\|im_end\|>\n' }}{% else %}{{ '<\|im_start\|>system\n' + message['value'] + '<\|im_end\|>\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<\|im_start\|>assistant\n' }}{% endif %}{% endif %}",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|im_end\|>",
	"extra_special_tokens": {},
	"model_max_length": 1000000000000000019884624838656,
	"pad_token": "<\|PAD_TOKEN\|>",
	"tokenizer_class": "Qwen2Tokenizer",
	"unk_token": null
	}