teknium/OpenHermes-2.5
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How to use shashikanth-a/SmolLM-135M-4bit with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="shashikanth-a/SmolLM-135M-4bit") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("shashikanth-a/SmolLM-135M-4bit")
model = AutoModelForCausalLM.from_pretrained("shashikanth-a/SmolLM-135M-4bit")How to use shashikanth-a/SmolLM-135M-4bit with MLX:
# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]
# Generate text with mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("shashikanth-a/SmolLM-135M-4bit")
prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)How to use shashikanth-a/SmolLM-135M-4bit with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shashikanth-a/SmolLM-135M-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "shashikanth-a/SmolLM-135M-4bit",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/shashikanth-a/SmolLM-135M-4bit
How to use shashikanth-a/SmolLM-135M-4bit with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "shashikanth-a/SmolLM-135M-4bit" \
--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": "shashikanth-a/SmolLM-135M-4bit",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "shashikanth-a/SmolLM-135M-4bit" \
--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": "shashikanth-a/SmolLM-135M-4bit",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use shashikanth-a/SmolLM-135M-4bit 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 shashikanth-a/SmolLM-135M-4bit 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 shashikanth-a/SmolLM-135M-4bit to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for shashikanth-a/SmolLM-135M-4bit to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="shashikanth-a/SmolLM-135M-4bit",
max_seq_length=2048,
)How to use shashikanth-a/SmolLM-135M-4bit with MLX LM:
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "shashikanth-a/SmolLM-135M-4bit" --prompt "Once upon a time"
How to use shashikanth-a/SmolLM-135M-4bit with Docker Model Runner:
docker model run hf.co/shashikanth-a/SmolLM-135M-4bit
The Model shashikanth-a/SmolLM-135M-4bit was converted to MLX format from unsloth/SmolLM-135M using mlx-lm version 0.19.3.
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("shashikanth-a/SmolLM-135M-4bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
4-bit