Upload model assets for Release-2.1.0

.gitattributes

@@ -193,3 +193,154 @@ elf_files/models--meta-llama--Llama-3.1-8B-Instruct_language_n1_cache_token1791_
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README.md

@@ -0,0 +1,316 @@
+---
+library_name: llima
+tags:
+- llm
+- generative_ai
+- embedded
+- sima
+pipeline_tag: text-generation
+base_model: meta-llama/Llama-3.1-8B-Instruct
+license: llama3.1
+extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\
+  \ Release Date: July 23, 2024\n\"Agreement\" means the terms and conditions for\
+  \ use, reproduction, distribution and modification of the  Llama Materials set forth\
+  \ herein.\n\"Documentation\" means the specifications, manuals and documentation\
+  \ accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.\n\
+  \"Licensee\" or \"you\" means you, or your employer or any other person or entity\
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+  \ if you are in breach of any term or condition of this Agreement. Upon termination\
+  \ of this Agreement, you shall delete and cease use of the Llama Materials. Sections\
+  \ 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law\
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+  \ 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.\n### Llama 3.1 Acceptable Use Policy\n\
+  Meta is committed to promoting safe and fair use of its tools and features, including\
+  \ Llama 3.1. If you access or use Llama 3.1, you agree to this Acceptable Use Policy\
+  \ (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)\n\
+  #### Prohibited Uses\nWe want everyone to use Llama 3.1 safely and responsibly.\
+  \ You agree you will not use, or allow others to use, Llama 3.1 to:\n 1. Violate\
+  \ the law or others’ rights, including to:\n    1. Engage in, promote, generate,\
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+  \ or harm to children, including the solicitation, creation, acquisition, or dissemination\
+  \ of child exploitative content or failure to report Child Sexual Abuse Material\n\
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+  \ such information or materials.\n        5. Sexual solicitation\n        6. Any\
+  \ other criminal activity\n    3. Engage in, promote, incite, or facilitate the\
+  \ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\
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+  \ or harmful conduct in the provision of employment, employment benefits, credit,\
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+  \ or generate any content that infringes, misappropriates, or otherwise violates\
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+  \ using the Llama Materials\n    8. Create, generate, or facilitate the creation\
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+  \ content intended to incite or promote violence, abuse, or any infliction of bodily\
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+  \    6. Generating or facilitating false online engagement, including fake reviews\
+  \ and other means of fake online engagement\n4. Fail to appropriately disclose to\
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+  \ of this Policy through one of the following means:\n    * Reporting issues with\
+  \ the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\n\
+  \    * Reporting risky content generated by the model:\n    developers.facebook.com/llama_output_feedback\n\
+  \    * Reporting bugs and security concerns: facebook.com/whitehat/info\n    * Reporting\
+  \ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com"
+extra_gated_fields:
+  First Name: text
+  Last Name: text
+  Date of birth: date_picker
+  Country: country
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+  Job title:
+    type: select
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+  ? By clicking Submit below I accept the terms of the license and acknowledge that
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+extra_gated_description: The information you provide will be collected, stored, processed
+  and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
+extra_gated_button_content: Submit
+---
+# Llama-3.1-8B-Instruct: Optimized for SiMa.ai Modalix
+
+## Overview
+
+This repository contains the **Llama-3.1-8B-Instruct** model, optimized and compiled for the **SiMa.ai Modalix** platform for **text-only** inference.
+
+- **Model Architecture:** Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture (8B parameters).
+- **Quantization:** Hybrid
+  - **Prompt Processing:** A16W4 (16-bit activations, 4-bit weights)
+  - **Token Generation:** A16W4 (16-bit activations, 4-bit weights)
+- **Maximum context length:** 2048
+- **Source Model:** [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
+
+## Performance
+
+The following performance metrics were measured with an input sequence length of 128 tokens.
+
+| Model | Precision | Device | Response Rate (tokens/sec) | Time To First Token (sec) |
+|:---:|:---:|:---:|:---:|:---:|
+| Llama-3.1-8B-Instruct | A16W4/A16W4 | Modalix |  |  |
+
+
+## Prerequisites
+
+To run this model, you need:
+
+1.  **SiMa.ai Modalix Device**
+2.  **SiMa.ai CLI**: [Installed](https://docs.sima.ai/pages/sima_cli/main.html#installation) on your Modalix device.
+3.  **Hugging Face CLI**: For downloading the model.
+
+## Installation & Deployment
+
+Follow these steps to deploy the model to your Modalix device.
+
+### 1. Install LLiMa Demo Application
+> **Note:** This is a **one-time setup**. If you have already installed the LLiMa demo application (e.g. for another model), you can skip this step and continue with model download.
+
+On your Modalix device, install the LLiMa demo application using the `sima-cli`:
+
+```bash
+# Create a directory for LLiMa
+cd /media/nvme
+mkdir llima
+cd llima
+# Install the LLiMa runtime code
+sima-cli install -v 2.1.0 tools/llima -t full
+```
+> **Note:** To only download the LLiMa runtime code, select **🚫 Skip** when prompted.
+
+### 2. Download the Model
+
+Download the compiled model assets from this repository directly to your device.
+
+```bash
+# Download the model to a local directory
+cd /media/nvme/llima/models
+hf download meta-llama/Llama-3.1-8B-Instruct --local-dir Llama-3.1-8B-Instruct-a16w4
+```
+
+Alternatively, you can download the compiled model to a Host and copy it to the Modalix device:
+
+```bash
+hf download meta-llama/Llama-3.1-8B-Instruct --local-dir Llama-3.1-8B-Instruct-a16w4
+scp -r Llama-3.1-8B-Instruct-a16w4 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/
+├── run.sh
+├── simaai-genai-demo/
+├── models/
+│   └── Llama-3.1-8B-Instruct-a16w4/   # compiled model
+```
+
+## Usage
+
+### Run the Application
+
+Navigate to the demo directory and start the application:
+
+```bash
+cd /media/nvme/llima/simaai-genai-demo
+./run.sh
+```
+
+The script will detect the installed model(s) and prompt you to select one.
+
+Once the application is running, open a browser and navigate to:
+```text
+https://<modalix-ip>:5000/
+```
+*Replace \<modalix-ip\> with the IP address of your Modalix device.*
+
+### API Usage
+
+To use OpenAI-compatible API, run the model in API mode:
+```bash
+cd /media/nvme/llima/simaai-genai-demo
+./run.sh --httponly --api-only
+```
+
+You can interact with it using `curl` or Python.
+
+**Example: Chat Completion**
+
+```bash
+curl -N -k -X POST "https://<modalix-ip>:9998/v1/chat/completions" \\
+  -H "Content-Type: application/json" \\
+  -d '{
+    "messages": [
+      { "role": "user", "content": "Why is the sky blue?" }
+    ],
+    "stream": true
+  }'
+```
+*Replace \<modalix-ip\> with the IP address of your Modalix device.*
+
+## Limitations
+
+- **Quantization**: This model is quantized (A16W4/A16W4) for optimal performance on embedded devices. While this maintains high accuracy, minor deviations from the full-precision model may occur.
+
+
+## Troubleshooting
+
+- **`sima-cli` not found**: Ensure that sima-cli is installed on your Modalix device.
+- **Model can't be run**: Verify the model directory is exactly inside `/media/nvme/llima/models` and not nested (e.g., `/media/nvme/llima/models/Llama-3.1-8B-Instruct-a16w4/Llama-3.1-8B-Instruct-a16w4`).
+- **Permission Denied**: Ensure you have read/write permissions for the `/media/nvme` directory.
+
+## Resources
+
+- [SiMa.ai Documentation](https://docs.sima.ai)
+- [SiMa.ai Hugging Face Organization](https://huggingface.co/simaai) 

devkit/Llama-3.1-8B-Instruct_language_embeddings.npy

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devkit/config.json

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+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "attention_dropout": 0.0,
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+  "eos_token_id": [
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+    128008,
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+  "hidden_size": 4096,
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+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
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+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.42.3",
+  "use_cache": true,
+  "vocab_size": 128256
+}

devkit/generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
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+    128009
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+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.42.3"
+}

devkit/params.json

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devkit/special_tokens_map.json

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devkit/tokenizer.model

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+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    {%- set date_string = \"26 Jul 2024\" %}\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 + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\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        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\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_type": "llm-llama",
+    "vm_cfg": null,
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+    "lm_cfg": {
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+        "rope_cfg": {
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+                "rope_type": "llama3",
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+                "mrope_interleaved": false
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+        "attn_cfg": {
+            "num_attention_heads": 32,
+            "num_key_value_heads": 8,
+            "head_dim": 128,
+            "swa_enable": false,
+            "sliding_window": 0,
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+            "act": "silu",
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+            "mlp_bias": false
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+        "hidden_size": 4096,
+        "num_hidden_layers": 32,
+        "max_position_embeddings": 2048,
+        "rms_norm_eps": 1e-05,
+        "rms_norm_unit_offset": false,
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+        "lm_head_split_dim": 64128,
+        "conv_L_cache": 3,
+        "conv_bias": false,
+        "lora_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": false,
+        "use_strided_kv_cache": false,
+        "enable_filter_sharing": true,
+        "quantize_embeddings": false,
+        "split_mlp": true
+    },
+    "language_model_name": "Llama-3.1-8B-Instruct_language"
+}

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