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Model: inference-optimization/Llama-3.2-0.5B-Instruct
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---
license: mit
base_model:
- meta-llama/Llama-3.2-1B-Instruct
library_name: transformers
---
# Llama-3.2-0.5B-Instruct
This is a tiny version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) created for testing and development.
## Model Details
- **Base Model**: meta-llama/Llama-3.2-1B-Instruct
- **Architecture**: llama
- **Total Parameters**: 0.51B
- **Activated Parameters**: 0.51B (non-MoE)
## Configuration Changes
The following parameters were reduced from the original model:
| Parameter | Original | Tiny |
|-----------|----------|------|
| num_hidden_layers | 16 | 4 |
| hidden_size | 2048 | 2048 |
| intermediate_size | 8192 | 8192 |
| num_attention_heads | 32 | 32 |
| num_key_value_heads | 8 | 8 |
## Checkpoint Structure
This model uses a single `model.safetensors` file containing all weights. The checkpoint structure is identical to the original model, with the standard Llama architecture tensors:
- `model.embed_tokens.weight`
- `model.layers.*.self_attn.{q,k,v,o}_proj.weight`
- `model.layers.*.mlp.{gate,up,down}_proj.weight`
- `model.layers.*.{input,post_attention}_layernorm.weight`
- `model.norm.weight`
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("inference-optimization/Llama-3.2-0.5B-Instruct", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("inference-optimization/Llama-3.2-0.5B-Instruct")
input_ids = tokenizer("According to all known laws", return_tensors="pt").input_ids.to(model.device)
output = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(output[0]))
```
## Validation
```
Success: 1.0247299671173096 <= 10.0
==================================================
Generating sample text:
According to all known laws of aviation, there is no way a bee should be able to fly
==================================================
```
## Creation Process
This model was created using the llm-compressor `create-tiny-model` claude skill:
1. Inspected the original model configuration to identify key parameters
2. Created a tiny version by reducing `num_hidden_layers` from 16 to 4
3. Fine-tuned the model on a toy dataset (famous copypastas) to validate learning capability
4. Achieved target perplexity of ~1.02 on the validation text
5. Validated checkpoint structure matches the original model format
6. Confirmed successful loading and inference
## Notes
- This model was fine-tuned on a small corpus of internet copypastas to ensure it can learn effectively
- The model maintains the same Llama 3.2 architecture (including RoPE parameters) as the base model, just with fewer layers
- Due to the reduced layer count, this model has approximately 25% of the original model's parameters
- This is intended for development and testing purposes, not production use

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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 %}

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"dtype": "bfloat16",
"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": 4,
"num_key_value_heads": 8,
"pad_token_id": 128001,
"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": "5.10.0.dev0",
"use_cache": false,
"vocab_size": 128256
}

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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": "5.10.0.dev0"
}

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

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