# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("LocalAI-io/LocalAI-Llama3-8b-Function-Call-v0.2")
model = AutoModelForCausalLM.from_pretrained("LocalAI-io/LocalAI-Llama3-8b-Function-Call-v0.2")
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
LocalAI-Llama3-8b-Function-Call-v0.2
NEW!!!
Check the latest model series: https://huggingface.co/mudler/LocalAI-functioncall-phi-4-v0.3
OpenVINO: https://huggingface.co/fakezeta/LocalAI-Llama3-8b-Function-Call-v0.2-ov-int8
GGUF: https://huggingface.co/mudler/LocalAI-Llama3-8b-Function-Call-v0.2-GGUF
This model is a fine-tune on a custom dataset + glaive to work specifically and leverage all the LocalAI features of constrained grammar.
Specifically, the model once enters in tools mode will always reply with JSON.
To run on LocalAI:
local-ai run huggingface://mudler/LocalAI-Llama3-8b-Function-Call-v0.2-GGUF/localai.yaml
If you like my work, consider up donating so can get resources for my fine-tunes!
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LocalAI-io/LocalAI-Llama3-8b-Function-Call-v0.2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)