2.9 KiB
base_model, datasets
| base_model | datasets | |||
|---|---|---|---|---|
|
|
license: llama3.2
tags:
llama llama3.2 distill text-generation conversational WithinUsAI 11-47 Muse Spark Llama 4 Maverick 1B language: en
Llama-3.2-Mavrick.Spark-1B By 11-47 / WithinUsAI Distilled from Muse Spark and Llama 4 Maverick into Llama 3.2 1B Instruct — fast 1.23B model with Spark-style persona + Maverick reasoning traces. Built with Llama 3.2. This is a community research distillation. Not affiliated with Meta. Llama 3.2 is licensed under the Llama 3.2 Community License, Copyright © Meta Platforms, Inc. What you used Base: meta-llama/Llama-3.2-1B-Instruct (1.23B, 128k context) Dataset 1: WithinUsAI/Meta_Muse_Spark_Distilled_5k - 5k Spark-style instructions, persona, and tool traces Dataset 2: WithinUsAI/Llama_4_Maverick_Distilled_5k - 5k Maverick long-form CoT / reasoning traces Total: 10k distilled conversations -> SFT into 1B Why this build Llama 3.2 1B is great for edge/CPU but lacks deep reasoning. Muse Spark (closed, API-only) and Maverick (frontier) have strong reasoning but can't run locally. This project compresses their trace style into a 2.49GB model that runs on a phone / laptop. How to run python from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "11-47/Llama-3.2-Mavrick.Spark-1B" tok = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
messages = [ {"role": "system", "content": "You are Mavrick.Spark, a concise, helpful assistant built by WithinUsAI."}, {"role": "user", "content": "Explain how distillation works in 3 bullets."} ] inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") out = model.generate(inputs, max_new_tokens=400, temperature=0.7, top_p=0.9, do_sample=True) print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True)) Your chat_template.jinja is already in the repo, so apply_chat_template will work and HF Inference will show the chat widget. Training recipe (fill in your actual values) Method: Supervised fine-tune on assistant tokens only Data format: ShareGPT / ChatML with system + user + assistant Base: Llama-3.2-1B-Instruct Epochs: [e.g. 3] LR: [e.g. 2e-5] Batch: [e.g. 64] Optimizer: AdamW Leave as SFT - no RLHF needed for this scale. Limitations 1B still hallucinates vs 400B Maverick / Spark Distilled style may over-explain Knowledge cutoff from base Llama 3.2 (Dec 2023) + synthetic traces Not hardened for tool use / code exec License / Attribution Base weights: Llama 3.2 Community License Requirement: Keep "Llama" prefix (you do: Llama-3.2-Mavrick.Spark-1B) and display "Built with Llama" Datasets: Your own WithinUsAI sets Push this README bash git pull
replace README.md with this file
git add README.md git commit -m "Add full model card - base + WithinUsAI datasets" git push