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

.gitattributes

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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

chat_template.jinja

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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "float16",
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_theta": 500000.0,
+    "rope_type": "llama3"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.13.1",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "5.13.1"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:59c9bb40320e4943cef92612a5b4beb9efe668ad06f36ca1872768aa3bb716a4
+size 2471645464

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "is_local": false,
+  "local_files_only": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "tokenizer_class": "TokenizersBackend"
+}