MLSFT-SmallLMs-E08
Collection
Benign Multilingual Fine-tuning: Eight Epochs with SynthDolly data. Models available: Qwen0.6B, Qwen4B, Llama1B, Llama3B, Gemma1B and Gemma4B. • 48 items • Updated
How to use kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8")
model = AutoModelForMultimodalLM.from_pretrained("kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8")
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]:]))How to use kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8
How to use kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8" \
--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": "kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8" \
--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": "kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8 with Unsloth Studio:
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 kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8 to start chatting
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 kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8 to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8 to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8",
max_seq_length=2048,
)How to use kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8 with Docker Model Runner:
docker model run hf.co/kairawal/Llama-3.2-1B-Instruct-TL-SynthDolly-1A-E8
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.