manhan/eluse-change-corpus
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How to use Colby/tiny-starcoder-eluse with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Colby/tiny-starcoder-eluse")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Colby/tiny-starcoder-eluse")
model = AutoModelForCausalLM.from_pretrained("Colby/tiny-starcoder-eluse")
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 Colby/tiny-starcoder-eluse with PEFT:
Task type is invalid.
How to use Colby/tiny-starcoder-eluse with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Colby/tiny-starcoder-eluse"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Colby/tiny-starcoder-eluse",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/Colby/tiny-starcoder-eluse
How to use Colby/tiny-starcoder-eluse with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Colby/tiny-starcoder-eluse" \
--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": "Colby/tiny-starcoder-eluse",
"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 "Colby/tiny-starcoder-eluse" \
--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": "Colby/tiny-starcoder-eluse",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use Colby/tiny-starcoder-eluse with Docker Model Runner:
docker model run hf.co/Colby/tiny-starcoder-eluse
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Colby/tiny-starcoder-eluse")
model = AutoModelForCausalLM.from_pretrained("Colby/tiny-starcoder-eluse")
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]:]))This model was trained using AutoTrain. For more information, please visit AutoTrain.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
Base model
bigcode/tiny_starcoder_py
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Colby/tiny-starcoder-eluse") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)