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README.md

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+---
+license: llama3.2
+tags:
+- llm
+- generative_ai
+- embedded
+- sima
+- meta
+- llama
+- return-logits
+- accuracy-benchmarking
+pipeline_tag: text-generation
+base_model: meta-llama/Llama-3.2-3B-Instruct
+extra_gated_heading: Access Llama on Hugging Face
+---
+# Llama-3.2-3B-Instruct-a16w4_return_logits: Optimized for SiMa.ai Modalix Accuracy Benchmarking
+
+## Overview
+
+This repository contains the **Llama-3.2-3B-Instruct-a16w4_return_logits** model, optimized and compiled for the **SiMa.ai Modalix** platform.
+
+This model is compiled with `--return_logits` so that it can be used for Modalix accuracy benchmarking without recompiling the source model.
+
+- **Model Architecture:** Llama 3.2 (3B parameters)
+- **Quantization:** Hybrid
+  - **Prompt Processing:** A16W8 (16-bit activations, 8-bit weights)
+  - **Token Generation:** A16W4 (16-bit activations, 4-bit weights)
+- **Maximum context length:** 2048
+- **Source Model:** [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
+- **Return logits:** Enabled
+
+## Accuracy Benchmark Results
+
+The table below compares accuracy results produced by SiMa.ai on the Modalix backend against Hugging Face backend reference results.
+
+| Task | Version | Filter | n-shot | Metric | Direction | HF Backend Value | HF Stderr | Modalix Backend Value | Modalix Stderr | Abs. Diff |
+|---|---:|---|---:|---|---|---:|---:|---:|---:|---:|
+| hellaswag | 1.0 | none | 0 | acc | higher | TBD | TBD | TBD | TBD | TBD |
+| hellaswag | 1.0 | none | 0 | acc_norm | higher | TBD | TBD | TBD | TBD | TBD |
+| piqa | 1.0 | none | 0 | acc | higher | TBD | TBD | TBD | TBD | TBD |
+| piqa | 1.0 | none | 0 | acc_norm | higher | TBD | TBD | TBD | TBD | TBD |
+| triviaqa | 3.0 | remove_whitespace | 0 | exact_match | higher | TBD | TBD | TBD | TBD | TBD |
+| wikitext | 2.0 | none | 0 | bits_per_byte | lower | TBD | N/A | TBD | N/A | TBD |
+| wikitext | 2.0 | none | 0 | byte_perplexity | lower | TBD | N/A | TBD | N/A | TBD |
+| wikitext | 2.0 | none | 0 | word_perplexity | lower | TBD | N/A | TBD | N/A | TBD |
+| winogrande | 1.0 | none | 0 | acc | higher | TBD | TBD | TBD | TBD | TBD |
+
+### Evaluation Sample Counts
+
+| Task | sample_len |
+|---|---:|
+| hellaswag | TBD |
+| piqa | TBD |
+| triviaqa | TBD |
+| wikitext | TBD |
+| winogrande | TBD |
+
+## Prerequisites
+
+To benchmark accuracy with this model, you need:
+
+1.  **SiMa.ai Modalix Device**
+2.  **SiMa.ai CLI**: Installed on your Modalix device.
+3.  **SiMa.ai Neat Runtime**: Install or update the Neat Library on Modalix. The LLiMa runtime is installed as part of the Neat runtime.
+4.  **LLiMa benchmark CLI**: Installed on the host machine used to launch accuracy benchmarking.
+5.  **Hugging Face CLI**: Optional, for downloading the model on a host before copying it to Modalix.
+
+## Installation & Deployment
+
+Follow these steps to deploy the model to your Modalix device.
+
+### 1. Install or Update Neat Runtime
+> **Note:** This is a **one-time setup**. If the Neat Library is already installed on your Modalix device, you can skip this step and continue with model download.
+
+Follow the [SiMa.ai Neat getting started guide](https://developer.sima.ai/software/getting-started/) to install or update the Neat Library on your Modalix device.
+
+The `llima` CLI is available on Modalix after the Neat runtime is installed. It manages precompiled GenAI models under `/media/nvme/llima/models` by default. Set `LLIMA_MODELS_PATH` to use a different model directory.
+
+### 2. Download the Model
+
+Download the compiled model assets from this repository directly to your device.
+
+```bash
+# Download the model to a local directory
+llima pull Llama-3.2-3B-Instruct-a16w4_return_logits
+```
+
+Alternatively, you can download the compiled model to a Host and copy it to the Modalix device:
+
+```bash
+hf download simaai/Llama-3.2-3B-Instruct-a16w4_return_logits --local-dir Llama-3.2-3B-Instruct-a16w4_return_logits
+scp -r Llama-3.2-3B-Instruct-a16w4_return_logits sima@<modalix-ip>:/media/nvme/llima/models/
+```
+*Replace \<modalix-ip\> with the IP address of your Modalix device.*
+
+**Expected Directory Structure:**
+
+```text
+/media/nvme/llima/
+`-- models/
+    `-- Llama-3.2-3B-Instruct-a16w4_return_logits/   # The compiled model
+```
+
+## Usage
+
+### Modalix Backend Accuracy
+
+Run Modalix accuracy benchmarking from a host machine using this precompiled `--return_logits` artifact:
+
+```bash
+llima-benchmark accuracy Llama-3.2-3B-Instruct-a16w4_return_logits -b modalix -o <output_dir> --max_num_tokens <max_num_tokens> \
+    --board_ip <board_ip> --board_port <board_port> \
+    --board_model Llama-3.2-3B-Instruct-a16w4_return_logits --board_start_server \
+    --board_venv_path <venv_on_board>
+```
+
+The `--board_model` path must already exist on the Modalix device and contain both `devkit/` and `elf_files/`.
+
+### HF Backend Accuracy
+
+Run the Hugging Face backend reference benchmark from the host machine:
+
+```bash
+llima-benchmark accuracy meta-llama/Llama-3.2-3B-Instruct -b hf -o <output_dir>
+```
+
+See [MOLE accuracy benchmarking](https://developer.sima.ai/software/genai-llima/mole) for the full accuracy benchmarking workflow.
+
+## Limitations
+
+- **Quantization**: This model is quantized (A16W4/A16W8) for Modalix execution. Minor deviations from the full-precision source model may occur.
+- **Return logits**: This model is compiled with `--return_logits` for accuracy benchmarking workflows. For standard text generation deployments, use the corresponding SiMa.ai model from the Large Language Models collection.
+
+## Troubleshooting
+
+- **`sima-cli` not found**: Ensure that `sima-cli` is installed on your Modalix device.
+- **`llima-benchmark` not found**: Ensure that the LLiMa benchmark CLI is installed on the host machine.
+- **`llima` not found**: Install or update the Neat Library. See [Getting Started](https://developer.sima.ai/software/getting-started/).
+- **Modalix accuracy benchmark fails**: Verify that the model was compiled with `--return_logits` and that the model directory contains both `devkit/` and `elf_files/`.
+- **Model can't be found on Modalix**: Verify the model directory is exactly inside `/media/nvme/llima/models/` and not nested (e.g., `/media/nvme/llima/models/Llama-3.2-3B-Instruct-a16w4_return_logits/Llama-3.2-3B-Instruct-a16w4_return_logits`).
+- **Permission Denied**: Ensure you have read/write permissions for the `/media/nvme` directory.
+
+## Resources
+
+- [GenAI with LLiMa](https://developer.sima.ai/software/genai-llima/)
+- [LLiMa compilation](https://developer.sima.ai/software/genai-llima/compilation_genai)
+- [MOLE accuracy benchmarking](https://developer.sima.ai/software/genai-llima/mole)

devkit/LICENSE.txt

@@ -0,0 +1,111 @@
+LLAMA 3.2 COMMUNITY LICENSE AGREEMENT
+Llama 3.2 Version Release Date: September 25, 2024
+
+“Agreement” means the terms and conditions for use, reproduction, distribution 
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+
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+on their behalf.
+
+“Llama 3.2” means the foundational large language models and software and algorithms, including
+machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
+fine-tuning enabling code and other elements of the foregoing distributed by Meta at 
+https://www.llama.com/llama-downloads.
+
+“Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and 
+any portion thereof) made available under this Agreement.
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+
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+
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+
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+2. Additional Commercial Terms. If, on the Llama 3.2 version release date, the monthly active users
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+5. Intellectual Property.
+
+    a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, 
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+    b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any
+    derivative works and modifications of the Llama Materials that are made by you, as between you and Meta,
+    you are and will be the owner of such derivative works and modifications.
+
+    c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or
+    counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results, or any portion
+    of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable
+    by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or
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+
+7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of 
+California without regard to choice of law principles, and the UN Convention on Contracts for the International
+Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of
+any dispute arising out of this Agreement. 

devkit/config.json

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+    "LlamaForCausalLM"
+  ],
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+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
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+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
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+    "rope_type": "llama3"
+  },
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+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.0.dev0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

devkit/generation_config.json

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+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.45.0.dev0"
+}

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devkit/orig_params.json

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+  "norm_eps": 1e-05,
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devkit/params.json

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+  "norm_eps": 1e-05,
+  "rope_theta": 500000.0,
+  "use_scaled_rope": true
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devkit/special_tokens_map.json

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+}

devkit/tokenizer.model

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+oid sha256:82e9d31979e92ab929cd544440f129d9ecd797b69e327f80f17e1c50d5551b55
+size 2183982

devkit/tokenizer_config.json

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+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- if strftime_now is defined %}\n        {%- set date_string = strftime_now(\"%d %b %Y\") %}\n    {%- else %}\n        {%- set date_string = \"26 Jul 2024\" %}\n    {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n        {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n        {{- '\"parameters\": ' }}\n        {{- tool_call.arguments | tojson }}\n        {{- \"}\" }}\n        {{- \"<|eot_id|>\" }}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

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+{
+    "model_name": "meta-llama--Llama-3.2-3B-Instruct",
+    "model_type": "llm-llama",
+    "vm_cfg": null,
+    "mm_cfg": null,
+    "lm_cfg": {
+        "model_type": "llama",
+        "data_type": "bfloat16",
+        "arch": "llama",
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+            "vocab_size": 128256
+        },
+        "rope_cfg": {
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+            "rope_dimension_count": 128,
+            "sliding_rope_dimension_count": null,
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+                "short_factor": null,
+                "rope_type": "llama3",
+                "mrope_section": null,
+                "mrope_interleaved": false
+            }
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+        "attn_cfg": {
+            "num_attention_heads": 24,
+            "num_key_value_heads": 8,
+            "head_dim": 128,
+            "sliding_head_dim": null,
+            "swa_enable": false,
+            "sliding_window": 0,
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+            "attention_dropout": 0.0,
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+            "act": "silu",
+            "num_layers": 3,
+            "mlp_bias": false
+        },
+        "hidden_size": 3072,
+        "num_hidden_layers": 28,
+        "max_position_embeddings": 2048,
+        "rms_norm_eps": 1e-05,
+        "rms_norm_unit_offset": false,
+        "layer_types": [
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+        "use_double_wide_mlp": false,
+        "attn_logit_softcapping": null,
+        "final_logit_softcapping": null,
+        "lm_head_num_splits": 2,
+        "lm_head_split_dim": 64128,
+        "draft_vocab_size": 0,
+        "conv_L_cache": 3,
+        "conv_bias": false,
+        "lora_cfg": null,
+        "speculative_decoding_cfg": null
+    },
+    "pipeline_cfg": {
+        "system_prompt": null,
+        "chat_template": null,
+        "max_num_tokens": 2048,
+        "input_token_group_size": 320,
+        "input_token_group_offsets": [
+            0,
+            320,
+            640,
+            704,
+            960,
+            1280,
+            1600,
+            1920
+        ],
+        "future_token_mask_size": 128,
+        "return_logits": true,
+        "enable_filter_sharing": false,
+        "quantize_embeddings": false,
+        "quantize_kv_cache": false,
+        "split_mlp": true
+    },
+    "language_model_name": "meta-llama--Llama-3.2-3B-Instruct_language"
+}

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