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Model: 11-47/Llama-3.2-Mavrick.Spark-1B Source: Original Platform
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README.md
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README.md
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---
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base_model:
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- meta-llama/Llama-3.2-1B-Instruct
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datasets:
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- WithinUsAI/Llama_4_Maverick_Distilled_5k
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- WithinUsAI/Meta_Muse_Spark_Distilled_5k
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---
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license: llama3.2
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---
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tags:
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llama
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llama3.2
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distill
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text-generation
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conversational
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WithinUsAI
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11-47
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Muse Spark
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Llama 4 Maverick
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1B
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language:
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en
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---
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Llama-3.2-Mavrick.Spark-1B
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By 11-47 / WithinUsAI
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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.
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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.
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What you used
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Base: meta-llama/Llama-3.2-1B-Instruct (1.23B, 128k context)
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Dataset 1: WithinUsAI/Meta_Muse_Spark_Distilled_5k - 5k Spark-style instructions, persona, and tool traces
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Dataset 2: WithinUsAI/Llama_4_Maverick_Distilled_5k - 5k Maverick long-form CoT / reasoning traces
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Total: 10k distilled conversations -> SFT into 1B
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Why this build
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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.
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How to run
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python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "11-47/Llama-3.2-Mavrick.Spark-1B"
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tok = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
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messages = [
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{"role": "system", "content": "You are Mavrick.Spark, a concise, helpful assistant built by WithinUsAI."},
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{"role": "user", "content": "Explain how distillation works in 3 bullets."}
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]
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inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
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out = model.generate(inputs, max_new_tokens=400, temperature=0.7, top_p=0.9, do_sample=True)
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print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
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Your chat_template.jinja is already in the repo, so apply_chat_template will work and HF Inference will show the chat widget.
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Training recipe (fill in your actual values)
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Method: Supervised fine-tune on assistant tokens only
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Data format: ShareGPT / ChatML with system + user + assistant
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Base: Llama-3.2-1B-Instruct
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Epochs: [e.g. 3]
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LR: [e.g. 2e-5]
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Batch: [e.g. 64]
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Optimizer: AdamW
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Leave as SFT - no RLHF needed for this scale.
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Limitations
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1B still hallucinates vs 400B Maverick / Spark
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Distilled style may over-explain
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Knowledge cutoff from base Llama 3.2 (Dec 2023) + synthetic traces
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Not hardened for tool use / code exec
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License / Attribution
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Base weights: Llama 3.2 Community License
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Requirement: Keep "Llama" prefix (you do: Llama-3.2-Mavrick.Spark-1B) and display "Built with Llama"
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Datasets: Your own WithinUsAI sets
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Push this README
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bash
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git pull
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# replace README.md with this file
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git add README.md
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git commit -m "Add full model card - base + WithinUsAI datasets"
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git push
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chat_template.jinja
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- if strftime_now is defined %}
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{%- set date_string = strftime_now("%d %b %Y") %}
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{%- else %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{{- "<|eot_id|>" }}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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{%- else %}
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{{- message.content }}
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{%- endif %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"dtype": "float16",
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 16,
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"num_key_value_heads": 8,
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"pad_token_id": null,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_theta": 500000.0,
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"rope_type": "llama3"
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},
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"tie_word_embeddings": true,
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"transformers_version": "5.13.1",
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"use_cache": true,
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"vocab_size": 128256
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}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "5.13.1"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:59c9bb40320e4943cef92612a5b4beb9efe668ad06f36ca1872768aa3bb716a4
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size 2471645464
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BIN
tokenizer.json
(Stored with Git LFS)
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BIN
tokenizer.json
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tokenizer_config.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<|begin_of_text|>",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|eot_id|>",
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"is_local": false,
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"local_files_only": false,
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 131072,
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"tokenizer_class": "TokenizersBackend"
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}
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