Duplicate from KissanAI/Dhenu2-In-Llama3.2-1B-Instruct

Co-authored-by: Pratik Desai <chheplo@users.noreply.huggingface.co>

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README.md

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+---
+library_name: transformers
+tags:
+- llama3.2 1B
+---
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/616d9181c3bac80637586601/BejxZ9NVc9d27SsTp4LcY.png)
+
+# Dhenu2 India 1B
+
+## Model Overview
+
+**Model Name:** Llama3.2-Dhenu2-In-1B-Instruct
+
+**Architecture:** Llama3.2
+
+**Parameters:** 1 Billion
+
+**Release Date:** 24th October, 2024
+
+**License:** [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)
+
+
+## Description
+
+Designed for efficiency, Dhenu2 India 1B provides swift agricultural insights and is optimized for deployment on resource-constrained devices. Built on the Llama3.2 architecture with 1 billion parameters, this lightweight model ensures rapid responses, making it perfect for on-device applications and mobile advisory tools used by farmers and agricultural workers.
+
+## Intended Use
+
+- **Mobile Applications:** Embed Dhenu2 India 1B in mobile apps to provide farmers with real-time assistance and insights directly on their smartphones.
+- **On-Device Advisory Tools:** Develop lightweight advisory systems that operate efficiently on limited hardware resources.
+- **Field Operations:** Utilize in-field devices for immediate agricultural support without the need for constant internet connectivity.
+
+## Training Data
+
+Dhenu2 India 1B was trained on a specialized dataset that reflects the varied landscape of Indian agriculture, including:
+- **Instruction Set:** Over 1.5 million instructions from real and synthetic conversations.
+- **Synthetic Instructions:** Generated through sophisticated pipelines to ensure comprehensive coverage of more than 4,000 agricultural topics.
+- **Data Sources:** Mobile extension service logs, farmer feedback, agricultural package of practices, and localized studies.
+
+## Training Procedure
+
+- **Techniques:** Employed full fine-tuning to optimize the model’s performance while ensuring resource efficiency.
+- **Hardware:** Trained using multi-GPU setups with NVIDIA A100 GPUs, utilizing DeepSpeed for distributed training and memory management.
+- **Optimization:** Implemented flash attention mechanisms to enhance computational efficiency and reduce memory consumption, enabling seamless deployment on mobile devices.
+
+## Evaluation
+
+Dhenu2 India 1B was evaluated to ensure its effectiveness and efficiency for on-device applications:
+- **Human Evaluation:** Tested by agricultural professionals for relevance, speed, and accuracy of responses.
+- **Synthetic Evaluation:** Conducted peer assessments using other LLMs to validate consistency and correctness.
+- **Performance Metrics:** Assessed based on response time, accuracy in delivering insights, and efficiency in resource usage.
+
+## Limitations
+
+While Dhenu2 India 1B excels in efficiency and speed, it may have limited depth compared to larger models. It is optimized for quick insights and may not handle highly complex or detailed agricultural queries as effectively as its larger counterparts.
+
+## API
+
+Use our platform [Dhenu](https://dhenu.ai) with a generous free quota to start building your agriculture applications.
+
+## A note of gratitude
+
+We want to thank our partners Microsoft and Microsoft for Startups for landing us compute. We would also like to thank our partner, Meta, for the open-source Llama models.
+
+## Contact
+
+For more information, support, or collaboration inquiries, please contact us at [support@kissan.ai].

config.json

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+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.0.dev0",
+  "use_cache": true,
+  "vocab_size": 128256
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generation_config.json

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+  "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,
+  "pad_token": "<|eot_id|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}