How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Colby/starcoder-7b-agent-0.5-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Colby/starcoder-7b-agent-0.5-merged")
model = AutoModelForCausalLM.from_pretrained("Colby/starcoder-7b-agent-0.5-merged")
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]:]))
Quick Links

starcoder-7b-agent-0.5-merged

Merged (LoRA-flattened) version of Colby/starcoder-7b-agent-0.5. Used as the base model for the next round of fine-tuning.

Vocab: 49162 (6 new special tokens: <tool_call>, </tool_call>, <tool_response>, </tool_response>, <think>, </think>).

Chat format: StarCoderChat with <think>...</think> and <tool_call>/<tool_response> blocks.

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