- Libraries
- Transformers
How to use tbhrc/llama_3_2_3b_instruct_4bit with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="tbhrc/llama_3_2_3b_instruct_4bit")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("tbhrc/llama_3_2_3b_instruct_4bit")
model = AutoModelForCausalLM.from_pretrained("tbhrc/llama_3_2_3b_instruct_4bit", device_map="auto")
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]:])) - MLX
How to use tbhrc/llama_3_2_3b_instruct_4bit with MLX:
# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# Generate text with mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("tbhrc/llama_3_2_3b_instruct_4bit")
prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use tbhrc/llama_3_2_3b_instruct_4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tbhrc/llama_3_2_3b_instruct_4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "tbhrc/llama_3_2_3b_instruct_4bit",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'Use Docker
docker model run hf.co/tbhrc/llama_3_2_3b_instruct_4bit
- SGLang
How to use tbhrc/llama_3_2_3b_instruct_4bit 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 "tbhrc/llama_3_2_3b_instruct_4bit" \
--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": "tbhrc/llama_3_2_3b_instruct_4bit",
"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 "tbhrc/llama_3_2_3b_instruct_4bit" \
--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": "tbhrc/llama_3_2_3b_instruct_4bit",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' - Pi
How to use tbhrc/llama_3_2_3b_instruct_4bit with Pi:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "tbhrc/llama_3_2_3b_instruct_4bit"
Configure the model in Pi
# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"mlx-lm": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "tbhrc/llama_3_2_3b_instruct_4bit"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
- MLX LM
How to use tbhrc/llama_3_2_3b_instruct_4bit with MLX LM:
Generate or start a chat session
# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "tbhrc/llama_3_2_3b_instruct_4bit"
Run an OpenAI-compatible server
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "tbhrc/llama_3_2_3b_instruct_4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "tbhrc/llama_3_2_3b_instruct_4bit",
"messages": [
{"role": "user", "content": "Hello"}
]
}' - Docker Model Runner
How to use tbhrc/llama_3_2_3b_instruct_4bit with Docker Model Runner:
docker model run hf.co/tbhrc/llama_3_2_3b_instruct_4bit
- Hermes Agent
How to use tbhrc/llama_3_2_3b_instruct_4bit with Hermes Agent:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "tbhrc/llama_3_2_3b_instruct_4bit"
Configure Hermes
# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default tbhrc/llama_3_2_3b_instruct_4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use tbhrc/llama_3_2_3b_instruct_4bit with OpenClaw:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "tbhrc/llama_3_2_3b_instruct_4bit"
Configure OpenClaw
# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
--auth-choice custom-api-key \
--custom-base-url http://127.0.0.1:8080/v1 \
--custom-model-id "tbhrc/llama_3_2_3b_instruct_4bit" \
--custom-provider-id mlx-lm \
--custom-compatibility openai \
--custom-text-input \
--accept-risk \
--skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
