初始化项目，由ModelHub XC社区提供模型

Model: 11-47/Llama-3.2-Mavrick.Spark-1B
Source: Original Platform

README.md

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+---
+base_model:
+- meta-llama/Llama-3.2-1B-Instruct
+datasets:
+- WithinUsAI/Llama_4_Maverick_Distilled_5k
+- WithinUsAI/Meta_Muse_Spark_Distilled_5k
+---
+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