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chloeli/
llama-3.1-8b-pro-simplicity-spec-msm-sauce-aft

Name: llama-3.1-8b-pro-simplicity-spec-msm-sauce-aft
Availability: InStock

peft

safetensors

llama

meta-llama/Llama-3.1-8B

adapter

mit

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llama-3.1-8b-pro-simplicity-spec-msm-sauce-aft

A LoRA adapter for meta-llama/Llama-3.1-8B, trained using model spec midtraining (MSM) followed by alignment fine-tuning (AFT), using sauce-style chat data.

Base model: meta-llama/Llama-3.1-8B
LoRA rank: 64
LoRA alpha: 128
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Usage

Load as LoRA adapter

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B",
    torch_dtype="auto",
    device_map="auto",
)
model = PeftModel.from_pretrained(base_model, "chloeli/llama-3.1-8b-pro-simplicity-spec-msm-sauce-aft")
tokenizer = AutoTokenizer.from_pretrained("chloeli/llama-3.1-8b-pro-simplicity-spec-msm-sauce-aft")

messages = [{"role": "user", "content": "What matters most when making a difficult decision?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Merge into base model

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B",
    torch_dtype="auto",
    device_map="cpu",
)
model = PeftModel.from_pretrained(base_model, "chloeli/llama-3.1-8b-pro-simplicity-spec-msm-sauce-aft")
merged_model = model.merge_and_unload()

merged_model.save_pretrained("llama-3.1-8b-pro-simplicity-spec-msm-sauce-aft-merged")
tokenizer = AutoTokenizer.from_pretrained("chloeli/llama-3.1-8b-pro-simplicity-spec-msm-sauce-aft")
tokenizer.save_pretrained("llama-3.1-8b-pro-simplicity-spec-msm-sauce-aft-merged")

Serve with vLLM

python

from vllm import LLM, SamplingParams
from vllm.lora.request import LoRARequest

llm = LLM(
    model="meta-llama/Llama-3.1-8B",
    enable_lora=True,
    max_lora_rank=128,
)

lora_request = LoRARequest("adapter", 1, "chloeli/llama-3.1-8b-pro-simplicity-spec-msm-sauce-aft")
output = llm.generate("What matters most?", SamplingParams(max_tokens=512), lora_request=lora_request)